{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eCf4GWFs0Px5"
      },
      "source": [
        "# Transformer approach to classify time pressure in political speech\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tVNZoqx3YOv_"
      },
      "source": [
        "  ## 1) Overview\n",
        "\n",
        "    1) Connect to drive and install/import packages\n",
        "    2) Pre-processing the training data\n",
        "    3) Preparing the prediction sample\n",
        "    4) Naive Bayes prediction as a benchmark\n",
        "    5) Transformers on the sentence level\n",
        "      5.1/2) Out of sample predicition\n",
        "    6) Transformers on the token level\n",
        "      6.1/2) Out of sample prediction\n",
        "    7) Illustrations"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9fyBg6oLX2GW"
      },
      "source": [
        "### 1.1) Mounting the drive\n",
        "\n",
        "Note: Adapt to your personal drive\n",
        "      - Make sure the required data and scripts are stored there\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "aiBvDK_SMDKc",
        "outputId": "7ba301ee-1f1b-45a9-c005-53ee0dda5f76"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "\n",
        "# Mount Google Drive to /content/gdrive\n",
        "drive.mount('/content/gdrive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ElNNqtqQX8h2"
      },
      "source": [
        "### 1.2) Install neccessary packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "dFbxwAdFMHXc",
        "outputId": "77a64fcf-26ad-4a23-a8a9-754dce73245f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  Attempting uninstall: fsspec\n",
            "    Found existing installation: fsspec 2024.10.0\n",
            "    Uninstalling fsspec-2024.10.0:\n",
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            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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            "\u001b[0mSuccessfully installed datasets-3.2.0 dill-0.3.8 fsspec-2024.9.0 multiprocess-0.70.16 torchtext-0.18.0 xxhash-3.5.0\n"
          ]
        }
      ],
      "source": [
        "#!pip uninstall torch torchaudio torchdata torchtext torchvision transformers accelerate\n",
        "!pip install torch torchvision torchaudio torchtext transformers accelerate datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MYbqfDLnYDA2"
      },
      "source": [
        "### 1.3) Import libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mkhSMrRpMKJU",
        "outputId": "cd672ccf-2182-4515-b20a-cc58f84c371d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "from google.colab import files\n",
        "from pprint import pprint\n",
        "import pandas as pd\n",
        "import os\n",
        "import shutil\n",
        "import numpy as np\n",
        "import json\n",
        "\n",
        "# dataset loading\n",
        "from datasets import load_dataset, DatasetDict\n",
        "\n",
        "# used to tokenize text\n",
        "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
        "\n",
        "# used to load the pre-trained model\n",
        "from transformers import AutoModelForSequenceClassification\n",
        "\n",
        "# used to finetune the pre-trained model\n",
        "from transformers import Trainer, TrainingArguments\n",
        "\n",
        "# used to build an evaluation metric\n",
        "from sklearn.metrics import precision_recall_fscore_support, balanced_accuracy_score\n",
        "\n",
        "# used for sentence splitting\n",
        "from nltk.tokenize import sent_tokenize\n",
        "import nltk\n",
        "\n",
        "# Download the Punkt tokenizer for sentence splitting\n",
        "nltk.download('punkt')\n",
        "\n",
        "\n",
        "# To install from source instead of the last release, comment the command above and uncomment the following one.\n",
        "# ! pip install git+https://github.com/huggingface/transformers.git"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZEDKbcEDXyli"
      },
      "source": [
        "### 1.4) Assessing the gold labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "collapsed": true,
        "id": "P90JTmcsML3u",
        "outputId": "d33d085f-ad8f-4dcb-f796-a43bf27f5b8d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<style scoped>\n",
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              "      <td>With such a policy, we have a good chance to b...</td>\n",
              "      <td>I am convinced that without a credible and vis...</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>Anita Gradin</td>\n",
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              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>From my perspective, the European integration ...</td>\n",
              "      <td>With such a policy, we have a good chance to b...</td>\n",
              "      <td>What would be the main features of a credible ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Anita Gradin</td>\n",
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              "      <td>3205.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "  .colab-df-quickchart {\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    fill: var(--disabled-fill-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "result_df",
              "summary": "{\n  \"name\": \"result_df\",\n  \"rows\": 5273,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20,\n        \"min\": 1,\n        \"max\": 68,\n        \"num_unique_values\": 68,\n        \"samples\": [\n          47,\n          17,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sentence\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5145,\n        \"samples\": [\n          \"[s] It is time for the EU and its Member States to act firmly  against it [resp_f].\",\n          \"The United States, Canada and Australia are more successful in attracting skilled migrants, than Europe.\",\n          \"But if it does lead to democratisation and a greater emphasis on the fair rule of law, it will definitely have an impact on the chances for refugees coming to these countries.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sentence Before\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3931,\n        \"samples\": [\n          \"For example through the development and implementation of a comprehensive Human Resources and Training Strategy.\",\n          \"[s] Migration can bring great benefits [stance_p\",\n          \"I want the EU's activities in this field to feed into the work of the Global Counter-Terrorism Forum (GCTF) which the US has proposed to launch in 2011.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sentence After\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3586,\n        \"samples\": [\n          \"I will tell you about a young man called Tusse.\",\n          \"The topics on today's agenda are very important.\",\n          \"More specifically, with regard to asylum, the Commission will be adopting a working document in the next few days in which it will explain how appropriate implementation of the existing instruments on international protection, and in particular the Geneva Convention, is likely to protect our asylum systems and our humanitarian tradition perfectly against infiltration by terrorists or criminals.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"manual_check\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"yes\",\n          \"no\",\n          \"resp_f\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"speaker\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"franco frattini\",\n          \"dimitris avramopoulos\",\n          \"Anita Gradin\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.603468443048055,\n        \"min\": 1995.0,\n        \"max\": 2023.0,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          2005.0,\n          2014.0,\n          1995.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 911.0269822294068,\n        \"min\": 395.0,\n        \"max\": 3737.0,\n        \"num_unique_values\": 54,\n        \"samples\": [\n          1202.0,\n          1022.0,\n          663.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"topic\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 35,\n        \"samples\": [\n          \"labor market\",\n          \"demography\",\n          \"corruption\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"harmonized_topic\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 27,\n        \"samples\": [\n          \"labour market\",\n          \"integration\",\n          \"demography\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tp_h\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"resp_f\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tp_l\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"resp_s\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stance_p\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3349320635285418,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stance_n\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.17149858514250874,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Unnamed: 16\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "# Specify the path within your Google Drive where the Excel file is located\n",
        "excel_file_path = '/content/gdrive/MyDrive/TimePressure/multilabel_gold_all_final.xlsx'\n",
        "\n",
        "\n",
        "# Load the Excel file into a DataFrame\n",
        "result_df = pd.read_excel(excel_file_path)\n",
        "\n",
        "# Display the DataFrame\n",
        "result_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1.5) Counting speeches and sentences"
      ],
      "metadata": {
        "id": "Jcxc3581vF74"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Count the number of unique speech_ids for annotated text\n",
        "nr_speech_ids = result_df['ID'].nunique()\n",
        "\n",
        "print(\"Number of unique annotated speech_ids:\", nr_speech_ids)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DXfSa0jQvbX_",
        "outputId": "e5e7b062-d8a1-4d32-8ae2-3fde07f89c65"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of unique annotated speech_ids: 68\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Assuming result_df is already defined and contains the columns tp_h, resp_f, tp_l, resp_s\n",
        "\n",
        "# Count the number of positive labels (==1) for each column\n",
        "positive_counts = {\n",
        "    'tp_h': result_df['tp_h'].sum(),\n",
        "    'resp_f': result_df['resp_f'].sum(),\n",
        "    'tp_l': result_df['tp_l'].sum(),\n",
        "    'resp_s': result_df['resp_s'].sum()\n",
        "}\n",
        "\n",
        "# Display the counts\n",
        "print(\"Count of positive labels (==1) for each column:\")\n",
        "for label, count in positive_counts.items():\n",
        "    print(f\"{label}: {count}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xSttutgM7TSb",
        "outputId": "c9dc0a51-37f5-47d8-d22b-2eeed0534070"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Count of positive labels (==1) for each column:\n",
            "tp_h: 149\n",
            "resp_f: 178\n",
            "tp_l: 103\n",
            "resp_s: 201\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zeQLdMSAY09S"
      },
      "source": [
        "## 2) Pre-processing the data\n",
        "\n",
        "In order to use the data with supervised LLMs, they need to be changed into a different format."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y_nAL3wYMkbk"
      },
      "source": [
        "### 2.1  Cleaning the annotation labels\n",
        "Note this is only for the Naive Bayes and sentence level classifier. For the token level we need to pre-process the data with annotation labels."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "1dl33H5VMmrg",
        "outputId": "73c9c7cb-19e8-4d40-87b0-9a1a271fda27"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                               Sentence  \\\n",
            "0     Mr, chairman, ladies, gentlemen and dear friends!   \n",
            "1     As the Commission member responsible for justi...   \n",
            "2     I am convinced that without a credible and vis...   \n",
            "3     With such a policy, we have a good chance to b...   \n",
            "4     From my perspective, the European integration ...   \n",
            "...                                                 ...   \n",
            "4708   In particular, between Greece, Serbia, the fo...   \n",
            "4709   [s] By the end of this week, 400 additional p...   \n",
            "4710   Frontex will assist Greece in the registratio...   \n",
            "4711   Countries wants to step up efforts to return ...   \n",
            "4712   To this end, the Commission stands ready to w...   \n",
            "\n",
            "                                       cleaned_sentence  \n",
            "0     Mr, chairman, ladies, gentlemen and dear friends!  \n",
            "1     As the Commission member responsible for justi...  \n",
            "2     I am convinced that without a credible and vis...  \n",
            "3     With such a policy, we have a good chance to b...  \n",
            "4     From my perspective, the European integration ...  \n",
            "...                                                 ...  \n",
            "4708   In particular, between Greece, Serbia, the fo...  \n",
            "4709    By the end of this week, 400 additional poli...  \n",
            "4710   Frontex will assist Greece in the registratio...  \n",
            "4711   Countries wants to step up efforts to return ...  \n",
            "4712   To this end, the Commission stands ready to w...  \n",
            "\n",
            "[4713 rows x 2 columns]\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import re\n",
        "\n",
        "# Function to remove sequences within \"[]\" including \"[]\"\n",
        "def remove_sequences(text):\n",
        "    return re.sub(r'\\[.*?\\]', '', str(text))  # Ensure text is converted to string\n",
        "\n",
        "# Assuming result_df is your DataFrame\n",
        "\n",
        "# Convert 'Sentence' column to string\n",
        "result_df['Sentence'] = result_df['Sentence'].astype(str)\n",
        "\n",
        "# Apply the function to the 'Sentence' column\n",
        "result_df['cleaned_sentence'] = result_df['Sentence'].apply(remove_sequences)\n",
        "\n",
        "# Display the DataFrame with the cleaned sentences\n",
        "print(result_df[['Sentence', 'cleaned_sentence']])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9hjKfi5FMucQ"
      },
      "source": [
        "### 2.2 Data transformation\n",
        "We need to transform the data into a structure that enables pre-processing to be finally used in a transformers application"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zXjNBRTNIWj0"
      },
      "outputs": [],
      "source": [
        "# Convert the DataFrame to JSON\n",
        "json_data = result_df.to_json(orient='records')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M_Of8vf5M2Hu",
        "outputId": "88e93175-ac8e-4303-b586-49743918ee80"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of rows: 4713\n",
            "Number of columns: 17\n"
          ]
        }
      ],
      "source": [
        "# Check the shape of the DataFrame\n",
        "df_shape = result_df.shape\n",
        "# Display the number of rows and columns\n",
        "num_rows, num_columns = df_shape\n",
        "print(f\"Number of rows: {num_rows}\")\n",
        "print(f\"Number of columns: {num_columns}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "c3oNiT5HM4XX",
        "outputId": "0abf3455-6266-42aa-c110-f3dff267f8ab"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     ID                                           Sentence  \\\n",
            "0     1  Mr, chairman, ladies, gentlemen and dear friends!   \n",
            "1     1  As the Commission member responsible for justi...   \n",
            "2     1  I am convinced that without a credible and vis...   \n",
            "3     1  With such a policy, we have a good chance to b...   \n",
            "4     1  From my perspective, the European integration ...   \n",
            "..   ..                                                ...   \n",
            "495   5  \"I am also currently engaged in drawing up a C...   \n",
            "496   5  One of the aims is to gain a better overall vi...   \n",
            "497   5  I therefore want to compile all existing arran...   \n",
            "498   5  I am thinking of proposing amendments to the e...   \n",
            "499   5  Last year the Commission started three pilot p...   \n",
            "\n",
            "                                       Sentence Before  \\\n",
            "0                                                 None   \n",
            "1    Mr, chairman, ladies, gentlemen and dear friends!   \n",
            "2    As the Commission member responsible for justi...   \n",
            "3    I am convinced that without a credible and vis...   \n",
            "4    With such a policy, we have a good chance to b...   \n",
            "..                                                 ...   \n",
            "495  The idea is to produce a model enabling EU Mem...   \n",
            "496  \"I am also currently engaged in drawing up a C...   \n",
            "497  One of the aims is to gain a better overall vi...   \n",
            "498  I therefore want to compile all existing arran...   \n",
            "499  I am thinking of proposing amendments to the e...   \n",
            "\n",
            "                                        Sentence After manual_check  \\\n",
            "0    As the Commission member responsible for justi...         None   \n",
            "1    I am convinced that without a credible and vis...         None   \n",
            "2    With such a policy, we have a good chance to b...         None   \n",
            "3    From my perspective, the European integration ...         None   \n",
            "4    What would be the main features of a credible ...         None   \n",
            "..                                                 ...          ...   \n",
            "495  One of the aims is to gain a better overall vi...         None   \n",
            "496  I therefore want to compile all existing arran...         None   \n",
            "497  I am thinking of proposing amendments to the e...         None   \n",
            "498  Last year the Commission started three pilot p...         None   \n",
            "499  This will result in a very ambitious multiannu...         None   \n",
            "\n",
            "          speaker    year  length topic harmonized_topic  tp_h  resp_f  tp_l  \\\n",
            "0    Anita Gradin  1995.0  3205.0  None             None     0       0     0   \n",
            "1    Anita Gradin  1995.0  3205.0  None             None     0       0     0   \n",
            "2    Anita Gradin  1995.0  3205.0  None             None     0       0     0   \n",
            "3    Anita Gradin  1995.0  3205.0  None             None     0       0     0   \n",
            "4    Anita Gradin  1995.0  3205.0  None             None     0       0     0   \n",
            "..            ...     ...     ...   ...              ...   ...     ...   ...   \n",
            "495  Anita Gradin  1997.0   840.0  None             None     0       0     0   \n",
            "496  Anita Gradin  1997.0   840.0  None             None     0       0     0   \n",
            "497  Anita Gradin  1997.0   840.0  None             None     0       0     0   \n",
            "498  Anita Gradin  1997.0   840.0  None             None     0       0     0   \n",
            "499  Anita Gradin  1997.0   840.0  None             None     0       0     0   \n",
            "\n",
            "     resp_s  stance_p  stance_n  \\\n",
            "0         0       NaN       NaN   \n",
            "1         0       NaN       NaN   \n",
            "2         0       NaN       NaN   \n",
            "3         0       NaN       NaN   \n",
            "4         0       NaN       NaN   \n",
            "..      ...       ...       ...   \n",
            "495       0       NaN       NaN   \n",
            "496       0       NaN       NaN   \n",
            "497       0       NaN       NaN   \n",
            "498       0       NaN       NaN   \n",
            "499       0       NaN       NaN   \n",
            "\n",
            "                                      cleaned_sentence  \n",
            "0    Mr, chairman, ladies, gentlemen and dear friends!  \n",
            "1    As the Commission member responsible for justi...  \n",
            "2    I am convinced that without a credible and vis...  \n",
            "3    With such a policy, we have a good chance to b...  \n",
            "4    From my perspective, the European integration ...  \n",
            "..                                                 ...  \n",
            "495  \"I am also currently engaged in drawing up a C...  \n",
            "496  One of the aims is to gain a better overall vi...  \n",
            "497  I therefore want to compile all existing arran...  \n",
            "498  I am thinking of proposing amendments to the e...  \n",
            "499  Last year the Commission started three pilot p...  \n",
            "\n",
            "[500 rows x 17 columns]\n"
          ]
        }
      ],
      "source": [
        "# Convert JSON string to Python object\n",
        "json_data_parsed = json.loads(json_data)\n",
        "\n",
        "# Convert Python object to Pandas DataFrame\n",
        "df = pd.DataFrame(json_data_parsed)\n",
        "\n",
        "snippet = df[:500]  # Display the first 500 characters for inspection\n",
        "pprint(snippet)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jnqZ4H_nM7Db"
      },
      "source": [
        "### 2.3 Defining train, test and evaluation splits\n",
        "Here we define the quantities for the three splits we need for our model.\n",
        "\n",
        "First, a training split to train the model\n",
        "A test split for evaluating model parameters\n",
        "An evaluation split to evaluate the performance\n",
        "NOTE: Typically samples are based on percentages.\n",
        "\n",
        "NOTE2: Reproducibility.\n",
        "\n",
        "In programming, randomness is governed by Random Number Generator (RNG) algorithms. You can control randomness by setting a so called \"seed\" that determines an RNG's initial state. By setting a seed at the beginning of your script, each random value you generate (e.g., like this) will be the same at each run of your script — as long as you run all the code in the script in the same order (e.g., cell by cell, from bottom to top)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HgISJ-HiM7i-",
        "outputId": "84df438f-117b-4261-d78b-5eddbbcc958a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of rows: 4713\n",
            "Number of columns: 17\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3045, 652, 652)"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "# Set a seed value for replicable random permutations\n",
        "seed_value = 1234\n",
        "np.random.seed(seed_value)\n",
        "\n",
        "# Reset indices of the original DataFrame\n",
        "result_df = result_df.reset_index(drop=True)\n",
        "\n",
        "# Check the shape of the DataFrame\n",
        "df_shape = result_df.shape\n",
        "\n",
        "# Display the number of rows and columns\n",
        "num_rows, num_columns = df_shape\n",
        "print(f\"Number of rows: {num_rows}\")\n",
        "print(f\"Number of columns: {num_columns}\")\n",
        "\n",
        "# Define the splits for training, dev, and test | We opt for a 70% / 15% / 15% split\n",
        "n_train = 3045\n",
        "n_dev = 652\n",
        "n_test = 652  # Adjust this value to ensure the sum is 4349\n",
        "\n",
        "if len(result_df) < n_train + n_dev + n_test:\n",
        "    raise ValueError(\"Not enough examples in the DataFrame for the specified splits.\")\n",
        "\n",
        "# Permute the indices with the specified seed\n",
        "idxs = np.random.permutation(len(result_df))\n",
        "\n",
        "# Split the indices into train, dev, and test\n",
        "train_idxs = idxs[:n_train]\n",
        "dev_idxs = idxs[n_train:(n_train + n_dev)]\n",
        "test_idxs = idxs[-n_test:]\n",
        "\n",
        "# Show the number of examples in each split\n",
        "len(train_idxs), len(dev_idxs), len(test_idxs)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "O3DwfFh5ND8g",
        "outputId": "8947be5e-308d-4b4e-d7a9-1d45c8a91471"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DataFrame size: 4713\n",
            "Train indices: 0 - 4712\n",
            "Dev indices: 22 - 4701\n",
            "Test indices: 3 - 4711\n",
            "      ID                                           Sentence  \\\n",
            "3201  38  If we all agree that we want stronger external...   \n",
            "2872  33  We agreed on EU rules to exchange passenger na...   \n",
            "3945  48  Fighting crimelike standing corps officer now ...   \n",
            "795    8  We see examples of this on an almost daily bas...   \n",
            "784    8  During the initial period of settlement in the...   \n",
            "...   ..                                                ...   \n",
            "1493  17                        Counter trafficking policy.   \n",
            "2260  24  According to the 2011 Transparency Internation...   \n",
            "3207  38  This is why the Commission has been talking wi...   \n",
            "3952  48                     About the future of your work.   \n",
            "1549  18  Policy must be devised that takes full account...   \n",
            "\n",
            "                                        Sentence Before  \\\n",
            "3201  [s] Before we can bolster our EU Agencies yet ...   \n",
            "2872               Progress was made on several fronts.   \n",
            "3945  Helping people - Like the officers who worked ...   \n",
            "795   While unemployment is still high it has been s...   \n",
            "784   In most cases the situation is complex and the...   \n",
            "...                                                 ...   \n",
            "1493  This is finally an opportunity to congratulate...   \n",
            "2260  That is why a common EU response is clearly ne...   \n",
            "3207                                  This is not fair.   \n",
            "3952  This conference today is about the future of E...   \n",
            "1549  We must all remain aware of the risks and cons...   \n",
            "\n",
            "                                         Sentence After manual_check  \\\n",
            "3201  Protecting our borders also means intervening ...          NaN   \n",
            "2872  We proposed the criminalisation of terrorist o...          NaN   \n",
            "3945  Because he found 10 kilos of smuggled gold wor...          NaN   \n",
            "795   At the same time Europe's population is declin...          NaN   \n",
            "784   For the individual receiving families they [s]...          NaN   \n",
            "...                                                 ...          ...   \n",
            "1493  Prevention of THB, protection of victims and e...          NaN   \n",
            "2260  Similarly, in a recent survey, to be published...          NaN   \n",
            "3207  The way I see this is as an agreement of count...          NaN   \n",
            "3952  We're building state of the art digital securi...          NaN   \n",
            "1549  We must also continue to address the needs of ...          NaN   \n",
            "\n",
            "                    speaker    year  length topic harmonized_topic  tp_h  \\\n",
            "3201  Dimitris Avramopoulos  2018.0  1264.0   NaN              NaN     0   \n",
            "2872  Dimitris Avramopoulos  2016.0  1938.0   NaN              NaN     0   \n",
            "3945         Ylva Johansson  2022.0   582.0   NaN              NaN     0   \n",
            "795      AntoÌnio Vitorino  2000.0  3737.0   NaN              NaN     0   \n",
            "784      AntoÌnio Vitorino  2000.0  3737.0   NaN              NaN     0   \n",
            "...                     ...     ...     ...   ...              ...   ...   \n",
            "1493        Franco Frattini  2005.0  1273.0   NaN              NaN     0   \n",
            "2260    Cecilia MalmstroÌˆm  2011.0  1357.0   NaN              NaN     0   \n",
            "3207  Dimitris Avramopoulos  2018.0  1264.0   NaN              NaN     0   \n",
            "3952         Ylva Johansson  2022.0   582.0   NaN              NaN     0   \n",
            "1549        Franco Frattini  2006.0  1712.0   NaN              NaN     0   \n",
            "\n",
            "      resp_f  tp_l  resp_s  stance_p  stance_n  \\\n",
            "3201       0     0       0       NaN       NaN   \n",
            "2872       0     0       0       NaN       NaN   \n",
            "3945       0     0       0       NaN       NaN   \n",
            "795        0     0       0       NaN       NaN   \n",
            "784        0     0       0       NaN       NaN   \n",
            "...      ...   ...     ...       ...       ...   \n",
            "1493       0     0       0       NaN       NaN   \n",
            "2260       0     0       0       NaN       NaN   \n",
            "3207       0     0       0       NaN       NaN   \n",
            "3952       0     0       0       NaN       NaN   \n",
            "1549       0     0       0       NaN       NaN   \n",
            "\n",
            "                                       cleaned_sentence  \n",
            "3201  If we all agree that we want stronger external...  \n",
            "2872  We agreed on EU rules to exchange passenger na...  \n",
            "3945  Fighting crimelike standing corps officer now ...  \n",
            "795   We see examples of this on an almost daily bas...  \n",
            "784   During the initial period of settlement in the...  \n",
            "...                                                 ...  \n",
            "1493                        Counter trafficking policy.  \n",
            "2260  According to the 2011 Transparency Internation...  \n",
            "3207  This is why the Commission has been talking wi...  \n",
            "3952                     About the future of your work.  \n",
            "1549  Policy must be devised that takes full account...  \n",
            "\n",
            "[3045 rows x 17 columns]\n",
            "      ID                                           Sentence  \\\n",
            "2289  24  It is also positive to note that the Commissio...   \n",
            "980   10  Its most recent update, in November, for the f...   \n",
            "4533  58   Indeed, [s] we all agree that time has come t...   \n",
            "1827  20  Integrated Border Management is crucial for im...   \n",
            "2281  24  Yet, worryingly, two thirds of Europeans appea...   \n",
            "...   ..                                                ...   \n",
            "2742  30              We need to show its positive impacts.   \n",
            "439    4  MULTIANNUAL PROGRAMMES This year the Commissio...   \n",
            "3591  43  [s] Covid forces us all to move into a higher ...   \n",
            "1037  10  I think it is sufficient to confirm the broad ...   \n",
            "3111  37  The Integrated Border Management Fund will equ...   \n",
            "\n",
            "                                        Sentence Before  \\\n",
            "2289  And the European Anti-Fraud Office (OLAF) will...   \n",
            "980   I cannot overemphasise the fact that it was de...   \n",
            "4533                                                NaN   \n",
            "1827  We must work together on common border and sec...   \n",
            "2281  For a start, the Communication pointed to how ...   \n",
            "...                                                 ...   \n",
            "2742  But we need to change the narrative on migration.   \n",
            "439   This is done within the Barcelona Process and ...   \n",
            "3591                         First, Digital transition.   \n",
            "1037        I don't think it's a question of substance.   \n",
            "3111  As you may remember, we also plan to reinforce...   \n",
            "\n",
            "                                         Sentence After manual_check  \\\n",
            "2289  These initiatives include: A new Anti-Fraud St...          NaN   \n",
            "980   On the other hand, the Commission could settle...          NaN   \n",
            "4533                                                NaN          NaN   \n",
            "1827  It facilitates the legitimate movement of peop...          NaN   \n",
            "2281                          So we must widen the net.          NaN   \n",
            "...                                                 ...          ...   \n",
            "2742  [s]Europe is an ageing continent and, without ...          NaN   \n",
            "439   This means that Multiannual Programmes have be...          NaN   \n",
            "3591  Many of you switched to offering services online.          yes   \n",
            "1037  But above all, with the support of all the dem...          NaN   \n",
            "3111  However, better managing borders is not just a...          NaN   \n",
            "\n",
            "                    speaker    year  length          topic harmonized_topic  \\\n",
            "2289    Cecilia MalmstroÌˆm  2011.0  1357.0            NaN              NaN   \n",
            "980      AntoÌnio Vitorino  2001.0  2026.0            NaN              NaN   \n",
            "4533  dimitris avramopoulos  2016.0     NaN            NaN              NaN   \n",
            "1827        Franco Frattini  2008.0  1202.0            NaN              NaN   \n",
            "2281    Cecilia MalmstroÌˆm  2011.0  1357.0            NaN              NaN   \n",
            "...                     ...     ...     ...            ...              ...   \n",
            "2742  Dimitris Avramopoulos  2014.0   497.0            NaN              NaN   \n",
            "439            Anita Gradin  1996.0  2039.0            NaN              NaN   \n",
            "3591         Ylva Johansson  2021.0  1572.0  labor markets    labor markets   \n",
            "1037     AntoÌnio Vitorino  2001.0  2026.0            NaN              NaN   \n",
            "3111  Dimitris Avramopoulos  2018.0  1129.0            NaN              NaN   \n",
            "\n",
            "      tp_h  resp_f  tp_l  resp_s  stance_p  stance_n  \\\n",
            "2289     0       0     0       0       NaN       NaN   \n",
            "980      0       0     0       0       NaN       NaN   \n",
            "4533     0       1     0       0       NaN       NaN   \n",
            "1827     0       0     0       0       NaN       NaN   \n",
            "2281     0       0     0       0       NaN       NaN   \n",
            "...    ...     ...   ...     ...       ...       ...   \n",
            "2742     0       0     0       0       NaN       NaN   \n",
            "439      0       0     0       0       NaN       NaN   \n",
            "3591     1       0     0       0       NaN       NaN   \n",
            "1037     0       0     0       0       NaN       NaN   \n",
            "3111     0       0     0       0       NaN       NaN   \n",
            "\n",
            "                                       cleaned_sentence  \n",
            "2289  It is also positive to note that the Commissio...  \n",
            "980   Its most recent update, in November, for the f...  \n",
            "4533   Indeed,  we all agree that time has come to r...  \n",
            "1827  Integrated Border Management is crucial for im...  \n",
            "2281  Yet, worryingly, two thirds of Europeans appea...  \n",
            "...                                                 ...  \n",
            "2742              We need to show its positive impacts.  \n",
            "439   MULTIANNUAL PROGRAMMES This year the Commissio...  \n",
            "3591   Covid forces us all to move into a higher gear .  \n",
            "1037  I think it is sufficient to confirm the broad ...  \n",
            "3111  The Integrated Border Management Fund will equ...  \n",
            "\n",
            "[652 rows x 17 columns]\n",
            "      ID                                           Sentence  \\\n",
            "604    6  We need to concentrate on prevention, not leas...   \n",
            "738    8  The reflections begin here today with this ina...   \n",
            "606    6  The joint project we are starting now with the...   \n",
            "2097  22  As from now the consent of the European Parlia...   \n",
            "2247  23                      Thank you for your attention.   \n",
            "...   ..                                                ...   \n",
            "664    7  Let me therefore point to a series of ongoing ...   \n",
            "3276  40  These are actions that have a tangible impact ...   \n",
            "1318  15  Since EUROPOL became fully operational in 1999...   \n",
            "723    7           Failure is not an option in these areas.   \n",
            "2863  33  But our humanitarian efforts and the better ma...   \n",
            "\n",
            "                                        Sentence Before  \\\n",
            "604   In all our activities, we focus on special areas.   \n",
            "738   In the context of this year's Jubilee Celebrat...   \n",
            "606   Co-operation with both sending and transit cou...   \n",
            "2097  It has assertively exercised its new prerogati...   \n",
            "2247  So in ten years' time, when the European crisi...   \n",
            "...                                                 ...   \n",
            "664   But of course work has not stood still since I...   \n",
            "3276                                   These are facts.   \n",
            "1318  This body has a vital part to play, in gatheri...   \n",
            "723   [s]  After Tampere, we have now the opportunit...   \n",
            "2863     Both are essential components of our response.   \n",
            "\n",
            "                                         Sentence After manual_check  \\\n",
            "604   Co-operation with both sending and transit cou...          NaN   \n",
            "738   They will continue in the autumn, with confere...          NaN   \n",
            "606         The recent conference in Moskow is another.          NaN   \n",
            "2097              And the TFTP vote was the first test.          NaN   \n",
            "2247                                                NaN          NaN   \n",
            "...                                                 ...          ...   \n",
            "664   Examination of the effectiveness of the Dublin...          NaN   \n",
            "3276       Dear all, I do not wish you to get me wrong.          NaN   \n",
            "1318  In response to the horrific terrorist attacks ...          NaN   \n",
            "723   The Commission, which, together with the Nethe...          NaN   \n",
            "2863  In December, we proposed the creation of a Eur...          NaN   \n",
            "\n",
            "                    speaker    year  length topic harmonized_topic  tp_h  \\\n",
            "604            Anita Gradin  1997.0  2058.0   NaN              NaN     0   \n",
            "738      AntoÌnio Vitorino  2000.0  3737.0   NaN              NaN     0   \n",
            "606            Anita Gradin  1997.0  2058.0   NaN              NaN     0   \n",
            "2097    Cecilia MalmstroÌˆm  2010.0  2062.0   NaN              NaN     0   \n",
            "2247    Cecilia MalmstroÌˆm  2011.0  1742.0   NaN              NaN     0   \n",
            "...                     ...     ...     ...   ...              ...   ...   \n",
            "664      AntoÌnio Vitorino  2000.0  2515.0   NaN              NaN     0   \n",
            "3276  Dimitris Avramopoulos  2019.0   862.0   NaN              NaN     0   \n",
            "1318     AntoÌnio Vitorino  2004.0  2343.0   NaN              NaN     0   \n",
            "723      AntoÌnio Vitorino  2000.0  2515.0   NaN              NaN     0   \n",
            "2863  Dimitris Avramopoulos  2016.0  1938.0   NaN              NaN     0   \n",
            "\n",
            "      resp_f  tp_l  resp_s  stance_p  stance_n  \\\n",
            "604        0     0       0       NaN       NaN   \n",
            "738        0     0       0       NaN       NaN   \n",
            "606        0     0       0       NaN       NaN   \n",
            "2097       0     0       0       NaN       NaN   \n",
            "2247       0     0       0       NaN       NaN   \n",
            "...      ...   ...     ...       ...       ...   \n",
            "664        0     0       0       NaN       NaN   \n",
            "3276       0     0       0       NaN       NaN   \n",
            "1318       0     0       0       NaN       NaN   \n",
            "723        0     0       0       NaN       NaN   \n",
            "2863       0     0       0       NaN       NaN   \n",
            "\n",
            "                                       cleaned_sentence  \n",
            "604   We need to concentrate on prevention, not leas...  \n",
            "738   The reflections begin here today with this ina...  \n",
            "606   The joint project we are starting now with the...  \n",
            "2097  As from now the consent of the European Parlia...  \n",
            "2247                      Thank you for your attention.  \n",
            "...                                                 ...  \n",
            "664   Let me therefore point to a series of ongoing ...  \n",
            "3276  These are actions that have a tangible impact ...  \n",
            "1318  Since EUROPOL became fully operational in 1999...  \n",
            "723            Failure is not an option in these areas.  \n",
            "2863  But our humanitarian efforts and the better ma...  \n",
            "\n",
            "[652 rows x 17 columns]\n"
          ]
        }
      ],
      "source": [
        "# Print DataFrame size\n",
        "print(\"DataFrame size:\", len(result_df))\n",
        "\n",
        "# Print ranges of indices for debugging\n",
        "print(\"Train indices:\", min(train_idxs), \"-\", max(train_idxs))\n",
        "print(\"Dev indices:\", min(dev_idxs), \"-\", max(dev_idxs))\n",
        "print(\"Test indices:\", min(test_idxs), \"-\", max(test_idxs))\n",
        "\n",
        "# Use the correct DataFrame for train, dev, and test splits\n",
        "train_df = result_df.iloc[train_idxs]\n",
        "dev_df = result_df.iloc[dev_idxs]\n",
        "test_df = result_df.iloc[test_idxs]\n",
        "\n",
        "# Display a snippet of the resulting DataFrames\n",
        "pprint(train_df)\n",
        "pprint(dev_df)\n",
        "pprint(test_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "syINBJUDNG0k",
        "outputId": "7dd38980-f8b5-4e2f-80b6-db44592dfefb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      ID                                           Sentence  \\\n",
            "3201  38  If we all agree that we want stronger external...   \n",
            "2872  33  We agreed on EU rules to exchange passenger na...   \n",
            "3945  48  Fighting crimelike standing corps officer now ...   \n",
            "795    8  We see examples of this on an almost daily bas...   \n",
            "784    8  During the initial period of settlement in the...   \n",
            "...   ..                                                ...   \n",
            "1493  17                        Counter trafficking policy.   \n",
            "2260  24  According to the 2011 Transparency Internation...   \n",
            "3207  38  This is why the Commission has been talking wi...   \n",
            "3952  48                     About the future of your work.   \n",
            "1549  18  Policy must be devised that takes full account...   \n",
            "\n",
            "                                        Sentence Before  \\\n",
            "3201  [s] Before we can bolster our EU Agencies yet ...   \n",
            "2872               Progress was made on several fronts.   \n",
            "3945  Helping people - Like the officers who worked ...   \n",
            "795   While unemployment is still high it has been s...   \n",
            "784   In most cases the situation is complex and the...   \n",
            "...                                                 ...   \n",
            "1493  This is finally an opportunity to congratulate...   \n",
            "2260  That is why a common EU response is clearly ne...   \n",
            "3207                                  This is not fair.   \n",
            "3952  This conference today is about the future of E...   \n",
            "1549  We must all remain aware of the risks and cons...   \n",
            "\n",
            "                                         Sentence After manual_check  \\\n",
            "3201  Protecting our borders also means intervening ...          NaN   \n",
            "2872  We proposed the criminalisation of terrorist o...          NaN   \n",
            "3945  Because he found 10 kilos of smuggled gold wor...          NaN   \n",
            "795   At the same time Europe's population is declin...          NaN   \n",
            "784   For the individual receiving families they [s]...          NaN   \n",
            "...                                                 ...          ...   \n",
            "1493  Prevention of THB, protection of victims and e...          NaN   \n",
            "2260  Similarly, in a recent survey, to be published...          NaN   \n",
            "3207  The way I see this is as an agreement of count...          NaN   \n",
            "3952  We're building state of the art digital securi...          NaN   \n",
            "1549  We must also continue to address the needs of ...          NaN   \n",
            "\n",
            "                    speaker    year  length topic harmonized_topic  tp_h  \\\n",
            "3201  Dimitris Avramopoulos  2018.0  1264.0   NaN              NaN     0   \n",
            "2872  Dimitris Avramopoulos  2016.0  1938.0   NaN              NaN     0   \n",
            "3945         Ylva Johansson  2022.0   582.0   NaN              NaN     0   \n",
            "795      AntoÌnio Vitorino  2000.0  3737.0   NaN              NaN     0   \n",
            "784      AntoÌnio Vitorino  2000.0  3737.0   NaN              NaN     0   \n",
            "...                     ...     ...     ...   ...              ...   ...   \n",
            "1493        Franco Frattini  2005.0  1273.0   NaN              NaN     0   \n",
            "2260    Cecilia MalmstroÌˆm  2011.0  1357.0   NaN              NaN     0   \n",
            "3207  Dimitris Avramopoulos  2018.0  1264.0   NaN              NaN     0   \n",
            "3952         Ylva Johansson  2022.0   582.0   NaN              NaN     0   \n",
            "1549        Franco Frattini  2006.0  1712.0   NaN              NaN     0   \n",
            "\n",
            "      resp_f  tp_l  resp_s  stance_p  stance_n  \\\n",
            "3201       0     0       0       NaN       NaN   \n",
            "2872       0     0       0       NaN       NaN   \n",
            "3945       0     0       0       NaN       NaN   \n",
            "795        0     0       0       NaN       NaN   \n",
            "784        0     0       0       NaN       NaN   \n",
            "...      ...   ...     ...       ...       ...   \n",
            "1493       0     0       0       NaN       NaN   \n",
            "2260       0     0       0       NaN       NaN   \n",
            "3207       0     0       0       NaN       NaN   \n",
            "3952       0     0       0       NaN       NaN   \n",
            "1549       0     0       0       NaN       NaN   \n",
            "\n",
            "                                       cleaned_sentence  \n",
            "3201  If we all agree that we want stronger external...  \n",
            "2872  We agreed on EU rules to exchange passenger na...  \n",
            "3945  Fighting crimelike standing corps officer now ...  \n",
            "795   We see examples of this on an almost daily bas...  \n",
            "784   During the initial period of settlement in the...  \n",
            "...                                                 ...  \n",
            "1493                        Counter trafficking policy.  \n",
            "2260  According to the 2011 Transparency Internation...  \n",
            "3207  This is why the Commission has been talking wi...  \n",
            "3952                     About the future of your work.  \n",
            "1549  Policy must be devised that takes full account...  \n",
            "\n",
            "[3045 rows x 17 columns]\n",
            "      ID                                           Sentence  \\\n",
            "2289  24  It is also positive to note that the Commissio...   \n",
            "980   10  Its most recent update, in November, for the f...   \n",
            "4533  58   Indeed, [s] we all agree that time has come t...   \n",
            "1827  20  Integrated Border Management is crucial for im...   \n",
            "2281  24  Yet, worryingly, two thirds of Europeans appea...   \n",
            "...   ..                                                ...   \n",
            "2742  30              We need to show its positive impacts.   \n",
            "439    4  MULTIANNUAL PROGRAMMES This year the Commissio...   \n",
            "3591  43  [s] Covid forces us all to move into a higher ...   \n",
            "1037  10  I think it is sufficient to confirm the broad ...   \n",
            "3111  37  The Integrated Border Management Fund will equ...   \n",
            "\n",
            "                                        Sentence Before  \\\n",
            "2289  And the European Anti-Fraud Office (OLAF) will...   \n",
            "980   I cannot overemphasise the fact that it was de...   \n",
            "4533                                                NaN   \n",
            "1827  We must work together on common border and sec...   \n",
            "2281  For a start, the Communication pointed to how ...   \n",
            "...                                                 ...   \n",
            "2742  But we need to change the narrative on migration.   \n",
            "439   This is done within the Barcelona Process and ...   \n",
            "3591                         First, Digital transition.   \n",
            "1037        I don't think it's a question of substance.   \n",
            "3111  As you may remember, we also plan to reinforce...   \n",
            "\n",
            "                                         Sentence After manual_check  \\\n",
            "2289  These initiatives include: A new Anti-Fraud St...          NaN   \n",
            "980   On the other hand, the Commission could settle...          NaN   \n",
            "4533                                                NaN          NaN   \n",
            "1827  It facilitates the legitimate movement of peop...          NaN   \n",
            "2281                          So we must widen the net.          NaN   \n",
            "...                                                 ...          ...   \n",
            "2742  [s]Europe is an ageing continent and, without ...          NaN   \n",
            "439   This means that Multiannual Programmes have be...          NaN   \n",
            "3591  Many of you switched to offering services online.          yes   \n",
            "1037  But above all, with the support of all the dem...          NaN   \n",
            "3111  However, better managing borders is not just a...          NaN   \n",
            "\n",
            "                    speaker    year  length          topic harmonized_topic  \\\n",
            "2289    Cecilia MalmstroÌˆm  2011.0  1357.0            NaN              NaN   \n",
            "980      AntoÌnio Vitorino  2001.0  2026.0            NaN              NaN   \n",
            "4533  dimitris avramopoulos  2016.0     NaN            NaN              NaN   \n",
            "1827        Franco Frattini  2008.0  1202.0            NaN              NaN   \n",
            "2281    Cecilia MalmstroÌˆm  2011.0  1357.0            NaN              NaN   \n",
            "...                     ...     ...     ...            ...              ...   \n",
            "2742  Dimitris Avramopoulos  2014.0   497.0            NaN              NaN   \n",
            "439            Anita Gradin  1996.0  2039.0            NaN              NaN   \n",
            "3591         Ylva Johansson  2021.0  1572.0  labor markets    labor markets   \n",
            "1037     AntoÌnio Vitorino  2001.0  2026.0            NaN              NaN   \n",
            "3111  Dimitris Avramopoulos  2018.0  1129.0            NaN              NaN   \n",
            "\n",
            "      tp_h  resp_f  tp_l  resp_s  stance_p  stance_n  \\\n",
            "2289     0       0     0       0       NaN       NaN   \n",
            "980      0       0     0       0       NaN       NaN   \n",
            "4533     0       1     0       0       NaN       NaN   \n",
            "1827     0       0     0       0       NaN       NaN   \n",
            "2281     0       0     0       0       NaN       NaN   \n",
            "...    ...     ...   ...     ...       ...       ...   \n",
            "2742     0       0     0       0       NaN       NaN   \n",
            "439      0       0     0       0       NaN       NaN   \n",
            "3591     1       0     0       0       NaN       NaN   \n",
            "1037     0       0     0       0       NaN       NaN   \n",
            "3111     0       0     0       0       NaN       NaN   \n",
            "\n",
            "                                       cleaned_sentence  \n",
            "2289  It is also positive to note that the Commissio...  \n",
            "980   Its most recent update, in November, for the f...  \n",
            "4533   Indeed,  we all agree that time has come to r...  \n",
            "1827  Integrated Border Management is crucial for im...  \n",
            "2281  Yet, worryingly, two thirds of Europeans appea...  \n",
            "...                                                 ...  \n",
            "2742              We need to show its positive impacts.  \n",
            "439   MULTIANNUAL PROGRAMMES This year the Commissio...  \n",
            "3591   Covid forces us all to move into a higher gear .  \n",
            "1037  I think it is sufficient to confirm the broad ...  \n",
            "3111  The Integrated Border Management Fund will equ...  \n",
            "\n",
            "[652 rows x 17 columns]\n",
            "      ID                                           Sentence  \\\n",
            "604    6  We need to concentrate on prevention, not leas...   \n",
            "738    8  The reflections begin here today with this ina...   \n",
            "606    6  The joint project we are starting now with the...   \n",
            "2097  22  As from now the consent of the European Parlia...   \n",
            "2247  23                      Thank you for your attention.   \n",
            "...   ..                                                ...   \n",
            "664    7  Let me therefore point to a series of ongoing ...   \n",
            "3276  40  These are actions that have a tangible impact ...   \n",
            "1318  15  Since EUROPOL became fully operational in 1999...   \n",
            "723    7           Failure is not an option in these areas.   \n",
            "2863  33  But our humanitarian efforts and the better ma...   \n",
            "\n",
            "                                        Sentence Before  \\\n",
            "604   In all our activities, we focus on special areas.   \n",
            "738   In the context of this year's Jubilee Celebrat...   \n",
            "606   Co-operation with both sending and transit cou...   \n",
            "2097  It has assertively exercised its new prerogati...   \n",
            "2247  So in ten years' time, when the European crisi...   \n",
            "...                                                 ...   \n",
            "664   But of course work has not stood still since I...   \n",
            "3276                                   These are facts.   \n",
            "1318  This body has a vital part to play, in gatheri...   \n",
            "723   [s]  After Tampere, we have now the opportunit...   \n",
            "2863     Both are essential components of our response.   \n",
            "\n",
            "                                         Sentence After manual_check  \\\n",
            "604   Co-operation with both sending and transit cou...          NaN   \n",
            "738   They will continue in the autumn, with confere...          NaN   \n",
            "606         The recent conference in Moskow is another.          NaN   \n",
            "2097              And the TFTP vote was the first test.          NaN   \n",
            "2247                                                NaN          NaN   \n",
            "...                                                 ...          ...   \n",
            "664   Examination of the effectiveness of the Dublin...          NaN   \n",
            "3276       Dear all, I do not wish you to get me wrong.          NaN   \n",
            "1318  In response to the horrific terrorist attacks ...          NaN   \n",
            "723   The Commission, which, together with the Nethe...          NaN   \n",
            "2863  In December, we proposed the creation of a Eur...          NaN   \n",
            "\n",
            "                    speaker    year  length topic harmonized_topic  tp_h  \\\n",
            "604            Anita Gradin  1997.0  2058.0   NaN              NaN     0   \n",
            "738      AntoÌnio Vitorino  2000.0  3737.0   NaN              NaN     0   \n",
            "606            Anita Gradin  1997.0  2058.0   NaN              NaN     0   \n",
            "2097    Cecilia MalmstroÌˆm  2010.0  2062.0   NaN              NaN     0   \n",
            "2247    Cecilia MalmstroÌˆm  2011.0  1742.0   NaN              NaN     0   \n",
            "...                     ...     ...     ...   ...              ...   ...   \n",
            "664      AntoÌnio Vitorino  2000.0  2515.0   NaN              NaN     0   \n",
            "3276  Dimitris Avramopoulos  2019.0   862.0   NaN              NaN     0   \n",
            "1318     AntoÌnio Vitorino  2004.0  2343.0   NaN              NaN     0   \n",
            "723      AntoÌnio Vitorino  2000.0  2515.0   NaN              NaN     0   \n",
            "2863  Dimitris Avramopoulos  2016.0  1938.0   NaN              NaN     0   \n",
            "\n",
            "      resp_f  tp_l  resp_s  stance_p  stance_n  \\\n",
            "604        0     0       0       NaN       NaN   \n",
            "738        0     0       0       NaN       NaN   \n",
            "606        0     0       0       NaN       NaN   \n",
            "2097       0     0       0       NaN       NaN   \n",
            "2247       0     0       0       NaN       NaN   \n",
            "...      ...   ...     ...       ...       ...   \n",
            "664        0     0       0       NaN       NaN   \n",
            "3276       0     0       0       NaN       NaN   \n",
            "1318       0     0       0       NaN       NaN   \n",
            "723        0     0       0       NaN       NaN   \n",
            "2863       0     0       0       NaN       NaN   \n",
            "\n",
            "                                       cleaned_sentence  \n",
            "604   We need to concentrate on prevention, not leas...  \n",
            "738   The reflections begin here today with this ina...  \n",
            "606   The joint project we are starting now with the...  \n",
            "2097  As from now the consent of the European Parlia...  \n",
            "2247                      Thank you for your attention.  \n",
            "...                                                 ...  \n",
            "664   Let me therefore point to a series of ongoing ...  \n",
            "3276  These are actions that have a tangible impact ...  \n",
            "1318  Since EUROPOL became fully operational in 1999...  \n",
            "723            Failure is not an option in these areas.  \n",
            "2863  But our humanitarian efforts and the better ma...  \n",
            "\n",
            "[652 rows x 17 columns]\n"
          ]
        }
      ],
      "source": [
        "# Use your json_data DataFrame for train, dev, and test splits\n",
        "train_df = result_df.iloc[train_idxs]\n",
        "dev_df = result_df.iloc[dev_idxs]\n",
        "test_df = result_df.iloc[test_idxs]\n",
        "\n",
        "# Display a snippet of the resulting DataFrames\n",
        "pprint(train_df)\n",
        "pprint(dev_df)\n",
        "pprint(test_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I_hgaUGaNJwk"
      },
      "source": [
        "### 2.4 Check that the splits have about equal label class distributions!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "PVy_qS_NNR8T",
        "outputId": "43797dd2-b2ea-456f-b8a3-e5b82db2f753"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Label Percentage:\n",
            "tp_h\n",
            "0    97.27422\n",
            "1     2.72578\n",
            "Name: proportion, dtype: float64\n",
            "\n",
            "Dev Label Percentage:\n",
            "tp_h\n",
            "0    96.779141\n",
            "1     3.220859\n",
            "Name: proportion, dtype: float64\n",
            "\n",
            "Test Label Percentage:\n",
            "tp_h\n",
            "0    96.932515\n",
            "1     3.067485\n",
            "Name: proportion, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# Calculate the percentage of each label in the splits\n",
        "train_label_percentage = train_df['tp_h'].value_counts(normalize=True) * 100\n",
        "dev_label_percentage = dev_df['tp_h'].value_counts(normalize=True) * 100\n",
        "test_label_percentage = test_df['tp_h'].value_counts(normalize=True) * 100\n",
        "\n",
        "# Display the results\n",
        "print(\"Train Label Percentage:\")\n",
        "print(train_label_percentage)\n",
        "\n",
        "print(\"\\nDev Label Percentage:\")\n",
        "print(dev_label_percentage)\n",
        "\n",
        "print(\"\\nTest Label Percentage:\")\n",
        "print(test_label_percentage)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the percentage of each label in the splits\n",
        "train_label_percentage = train_df['tp_l'].value_counts(normalize=True) * 100\n",
        "dev_label_percentage = dev_df['tp_l'].value_counts(normalize=True) * 100\n",
        "test_label_percentage = test_df['tp_l'].value_counts(normalize=True) * 100\n",
        "\n",
        "# Display the results\n",
        "print(\"Train Label Percentage:\")\n",
        "print(train_label_percentage)\n",
        "\n",
        "print(\"\\nDev Label Percentage:\")\n",
        "print(dev_label_percentage)\n",
        "\n",
        "print(\"\\nTest Label Percentage:\")\n",
        "print(test_label_percentage)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OzUj10ZMZLc6",
        "outputId": "a2d4a035-1cd7-4bcf-e25c-f460ed57a67c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Label Percentage:\n",
            "tp_l\n",
            "0    99.671593\n",
            "1     0.328407\n",
            "Name: proportion, dtype: float64\n",
            "\n",
            "Dev Label Percentage:\n",
            "tp_l\n",
            "0    99.693252\n",
            "1     0.306748\n",
            "Name: proportion, dtype: float64\n",
            "\n",
            "Test Label Percentage:\n",
            "tp_l\n",
            "0    99.846626\n",
            "1     0.153374\n",
            "Name: proportion, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the percentage of each label in the splits\n",
        "train_label_percentage = train_df['resp_f'].value_counts(normalize=True) * 100\n",
        "dev_label_percentage = dev_df['resp_f'].value_counts(normalize=True) * 100\n",
        "test_label_percentage = test_df['resp_f'].value_counts(normalize=True) * 100\n",
        "\n",
        "# Display the results\n",
        "print(\"Train Label Percentage:\")\n",
        "print(train_label_percentage)\n",
        "\n",
        "print(\"\\nDev Label Percentage:\")\n",
        "print(dev_label_percentage)\n",
        "\n",
        "print(\"\\nTest Label Percentage:\")\n",
        "print(test_label_percentage)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ex-lyW3XZZS5",
        "outputId": "a8d16479-8d7d-43de-9108-de0fc11adb91"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Label Percentage:\n",
            "resp_f\n",
            "0    97.241379\n",
            "1     2.758621\n",
            "Name: proportion, dtype: float64\n",
            "\n",
            "Dev Label Percentage:\n",
            "resp_f\n",
            "0    97.852761\n",
            "1     2.147239\n",
            "Name: proportion, dtype: float64\n",
            "\n",
            "Test Label Percentage:\n",
            "resp_f\n",
            "0    96.779141\n",
            "1     3.220859\n",
            "Name: proportion, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the percentage of each label in the splits\n",
        "train_label_percentage = train_df['resp_s'].value_counts(normalize=True) * 100\n",
        "dev_label_percentage = dev_df['resp_s'].value_counts(normalize=True) * 100\n",
        "test_label_percentage = test_df['resp_s'].value_counts(normalize=True) * 100\n",
        "\n",
        "# Display the results\n",
        "print(\"Train Label Percentage:\")\n",
        "print(train_label_percentage)\n",
        "\n",
        "print(\"\\nDev Label Percentage:\")\n",
        "print(dev_label_percentage)\n",
        "\n",
        "print(\"\\nTest Label Percentage:\")\n",
        "print(test_label_percentage)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SnjfNlU7ZoRK",
        "outputId": "f895f5ca-b088-4106-ddb8-49c77aac7ce5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Label Percentage:\n",
            "resp_s\n",
            "0    98.850575\n",
            "1     1.149425\n",
            "Name: proportion, dtype: float64\n",
            "\n",
            "Dev Label Percentage:\n",
            "resp_s\n",
            "0    99.079755\n",
            "1     0.920245\n",
            "Name: proportion, dtype: float64\n",
            "\n",
            "Test Label Percentage:\n",
            "resp_s\n",
            "0    99.079755\n",
            "1     0.920245\n",
            "Name: proportion, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3) Keynes analysis"
      ],
      "metadata": {
        "id": "1iZbET1Ys_24"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Specify the path within your Google Drive where the Excel file is located\n",
        "excel_file_path = '/content/gdrive/MyDrive/TimePressure/multilabel_gold.xlsx'\n",
        "\n",
        "\n",
        "# Load the Excel file into a DataFrame\n",
        "result_df = pd.read_excel(excel_file_path)\n",
        "\n",
        "# Display the DataFrame\n",
        "result_df.head(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "bXNzzCWj4-kZ",
        "outputId": "c680b4e5-dbfb-400d-8b1a-5ec91a061899"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   ID                                           Sentence Sentence Before  \\\n",
              "0   1  Mr, chairman, ladies, gentlemen and dear friends!             NaN   \n",
              "\n",
              "                                      Sentence After manual_check  \\\n",
              "0  As the Commission member responsible for justi...          NaN   \n",
              "\n",
              "        speaker    year  length topic harmonized_topic  tp_h  resp_f  tp_l  \\\n",
              "0  Anita Gradin  1995.0  3205.0   NaN              NaN     0       0     0   \n",
              "\n",
              "   resp_s  stance_p  stance_n  \n",
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              "summary": "{\n  \"name\": \"result_df\",\n  \"rows\": 4713,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 1,\n        \"max\": 61,\n        \"num_unique_values\": 61,\n        \"samples\": [\n          1,\n          6,\n          47\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sentence\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4653,\n        \"samples\": [\n          \"It is possible to make considerable changes to the working methods without altering the Treaty.\",\n          \"EU agencies - including the Fundamental Rights Agency and the Police Training College CEPOL - also provide guidance and training for law enforcement on respect for fundamental rights.\",\n          \"Three other multiannual programmes were decided last year: one in the field of justice named Grotius, one on forged documents, Sherlock, and one on the fight against sexual abuse of children, Stop.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sentence Before\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3931,\n        \"samples\": [\n          \"For example through the development and implementation of a comprehensive Human Resources and Training Strategy.\",\n          \"[s] Migration can bring great benefits [stance_p\",\n          \"I want the EU's activities in this field to feed into the work of the Global Counter-Terrorism Forum (GCTF) which the US has proposed to launch in 2011.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sentence After\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3586,\n        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\"speaker\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"franco frattini\",\n          \"dimitris avramopoulos\",\n          \"Anita Gradin\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.603468443048055,\n        \"min\": 1995.0,\n        \"max\": 2023.0,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          2005.0,\n          2014.0,\n          1995.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 911.0269822294068,\n        \"min\": 395.0,\n        \"max\": 3737.0,\n        \"num_unique_values\": 54,\n        \"samples\": [\n          1202.0,\n          1022.0,\n          663.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"topic\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 35,\n        \"samples\": [\n          \"labor market\",\n          \"demography\",\n          \"corruption\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"harmonized_topic\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 27,\n        \"samples\": [\n          \"labour market\",\n          \"integration\",\n          \"demography\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tp_h\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"resp_f\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tp_l\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"resp_s\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stance_p\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3349320635285418,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stance_n\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.17149858514250874,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FDR3OImg25yt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Keyword analysis for teh labelled spans"
      ],
      "metadata": {
        "id": "pQfDhuJfwDs5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import re\n",
        "from collections import Counter\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "import spacy\n",
        "\n",
        "# Download stopwords if not already downloaded\n",
        "nltk.download('stopwords')\n",
        "stop_words = set(stopwords.words('english'))\n",
        "\n",
        "# Load the English model for lemmatization\n",
        "nlp = spacy.load(\"en_core_web_sm\")\n",
        "\n",
        "# Step 1: Load the Excel file into a DataFrame\n",
        "excel_file_path = '/content/gdrive/MyDrive/TimePressure/multilabel_gold.xlsx'\n",
        "result_df = pd.read_excel(excel_file_path)\n",
        "\n",
        "# Ensure that the 'Sentence' column is treated as strings\n",
        "result_df['Sentence'] = result_df['Sentence'].astype(str)\n",
        "\n",
        "# Replace \"long term\" with \"long-term\" and \"medium term\" with \"medium-term\"\n",
        "result_df['Sentence'] = result_df['Sentence'].str.replace(r'\\blong term\\b', 'long-term', case=False, regex=True)\n",
        "result_df['Sentence'] = result_df['Sentence'].str.replace(r'\\bmedium term\\b', 'medium-term', case=False, regex=True)\n",
        "\n",
        "# Define a function to lemmatize words and remove stopwords\n",
        "def preprocess_words(words):\n",
        "    lemmatized_words = []\n",
        "    for word in words:\n",
        "        # Lemmatization\n",
        "        doc = nlp(word)\n",
        "        lemmatized_word = doc[0].lemma_.lower()  # Ensure case-insensitive comparison by lowering\n",
        "        # Remove stopwords\n",
        "        if lemmatized_word not in stop_words:\n",
        "            lemmatized_words.append(lemmatized_word)\n",
        "    return lemmatized_words\n",
        "\n",
        "# Define a function to extract spans from sentences for a given label\n",
        "def extract_words_within_spans(df, label):\n",
        "    pattern = r'\\[s\\](.*?)\\[' + label + r'\\]'\n",
        "    spans = df['Sentence'].str.extractall(pattern)\n",
        "    all_words_within_spans = spans[0].str.cat(sep=' ').split()\n",
        "    return preprocess_words(all_words_within_spans)\n",
        "\n",
        "# Step 1: Find the most frequent words in the spans for each label\n",
        "most_frequent_words_in_spans = {}\n",
        "for label in ['tp_h', 'tp_l', 'resp_f', 'resp_s']:\n",
        "    # Extract and preprocess words within spans\n",
        "    words_within_spans = extract_words_within_spans(result_df, label)\n",
        "\n",
        "    # Count word frequencies\n",
        "    word_counts = Counter(words_within_spans)\n",
        "\n",
        "    # Get the most frequent words (e.g., top 10)\n",
        "    most_frequent_words_in_spans[label] = word_counts.most_common(10)\n",
        "\n",
        "    # Print the most frequent words\n",
        "    print(f\"Most frequent words in {label} spans:\")\n",
        "    for word, count in most_frequent_words_in_spans[label]:\n",
        "        print(f\"  - {word}: {count}\")\n",
        "\n",
        "# Save the most frequent words for use in Step 2\n",
        "top_words_dict = most_frequent_words_in_spans\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Beo2YA4HujjJ",
        "outputId": "4b1fa81a-cd6c-4314-bb45-2cfe708b7db3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Most frequent words in tp_h spans:\n",
            "  - people: 16\n",
            "  - increase: 15\n",
            "  - population: 15\n",
            "  - need: 15\n",
            "  - europe: 11\n",
            "  - age: 11\n",
            "  - eu: 11\n",
            "  - today: 8\n",
            "  - migration: 8\n",
            "  - million: 8\n",
            "Most frequent words in tp_l spans:\n",
            "  - arrival: 5\n",
            "  - migration: 3\n",
            "  - compare: 3\n",
            "  - number: 3\n",
            "  - begin: 2\n",
            "  - europe: 2\n",
            "  - migrant: 2\n",
            "  - population: 2\n",
            "  - world: 2\n",
            "  - low: 2\n",
            "Most frequent words in resp_f spans:\n",
            "  - need: 25\n",
            "  - european: 13\n",
            "  - member: 13\n",
            "  - state: 13\n",
            "  - time: 10\n",
            "  - action: 10\n",
            "  - soon: 9\n",
            "  - possible: 9\n",
            "  - proposal: 8\n",
            "  - measure: 8\n",
            "Most frequent words in resp_s spans:\n",
            "  - need: 19\n",
            "  - long: 12\n",
            "  - migration: 10\n",
            "  - policy: 6\n",
            "  - require: 6\n",
            "  - eu: 5\n",
            "  - look: 5\n",
            "  - also: 4\n",
            "  - part: 4\n",
            "  - address: 4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 2: Calculate the percentage of the most frequent words in the entire dataset\n",
        "\n",
        "percentages = {}\n",
        "for label, top_words in top_words_dict.items():\n",
        "    # Concatenate all sentences and split into words\n",
        "    all_words_in_sentences = ' '.join(result_df['Sentence']).lower().split()\n",
        "\n",
        "    # Preprocess all words in the entire dataset\n",
        "    processed_all_words = preprocess_words(all_words_in_sentences)\n",
        "\n",
        "    # Count occurrences of all words in the dataset\n",
        "    all_words_count = Counter(processed_all_words)\n",
        "\n",
        "    # Calculate the percentage of each top word in the entire dataset\n",
        "    percentages[label] = {}\n",
        "    for word, count_in_spans in top_words:\n",
        "        total_count_in_corpus = all_words_count[word]\n",
        "        percentage = (count_in_spans / total_count_in_corpus * 100) if total_count_in_corpus > 0 else 0\n",
        "        percentages[label][word] = percentage\n",
        "\n",
        "    # Print the percentages\n",
        "    print(f\"Percentage of top words in {label} relative to total occurrences in the corpus:\")\n",
        "    for word, percentage in percentages[label].items():\n",
        "        print(f\"  - {word}: {percentage:.2f}%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "arB-itJpvAPl",
        "outputId": "8f1fec84-a0a6-4885-9972-f8d516f75b50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of top words in tp_h relative to total occurrences in the corpus:\n",
            "  - people: 6.93%\n",
            "  - increase: 19.74%\n",
            "  - population: 40.54%\n",
            "  - need: 3.41%\n",
            "  - europe: 4.35%\n",
            "  - age: 52.38%\n",
            "  - eu: 2.28%\n",
            "  - today: 5.33%\n",
            "  - migration: 1.82%\n",
            "  - million: 11.76%\n",
            "Percentage of top words in tp_l relative to total occurrences in the corpus:\n",
            "  - arrival: 23.81%\n",
            "  - migration: 0.68%\n",
            "  - compare: 30.00%\n",
            "  - number: 3.00%\n",
            "  - begin: 7.14%\n",
            "  - europe: 0.79%\n",
            "  - migrant: 0.91%\n",
            "  - population: 5.41%\n",
            "  - world: 2.38%\n",
            "  - low: 18.18%\n",
            "Percentage of top words in resp_f relative to total occurrences in the corpus:\n",
            "  - need: 5.68%\n",
            "  - european: 2.39%\n",
            "  - member: 2.98%\n",
            "  - state: 2.73%\n",
            "  - time: 6.67%\n",
            "  - action: 7.25%\n",
            "  - soon: 25.00%\n",
            "  - possible: 20.45%\n",
            "  - proposal: 8.51%\n",
            "  - measure: 10.26%\n",
            "Percentage of top words in resp_s relative to total occurrences in the corpus:\n",
            "  - need: 4.32%\n",
            "  - long: 16.22%\n",
            "  - migration: 2.28%\n",
            "  - policy: 1.90%\n",
            "  - require: 20.69%\n",
            "  - eu: 1.04%\n",
            "  - look: 10.00%\n",
            "  - also: 1.05%\n",
            "  - part: 3.57%\n",
            "  - address: 5.41%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Same for resampled annotations"
      ],
      "metadata": {
        "id": "8LmwCMnGvDqj"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gvM9P53xvHDq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import re\n",
        "from collections import Counter\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "import spacy\n",
        "\n",
        "# Download stopwords if not already downloaded\n",
        "nltk.download('stopwords')\n",
        "stop_words = set(stopwords.words('english'))\n",
        "\n",
        "# Load the English model for lemmatization\n",
        "nlp = spacy.load(\"en_core_web_sm\")\n",
        "\n",
        "# Step 1: Load the Excel file into a DataFrame\n",
        "excel_file_path = '/content/gdrive/MyDrive/TimePressure/sentences_corrected_resampled.xlsx'\n",
        "result_df = pd.read_excel(excel_file_path)\n",
        "\n",
        "# Ensure that the 'Sentence' column is treated as strings\n",
        "result_df['Sentence'] = result_df['Sentence'].astype(str)\n",
        "\n",
        "# Replace \"long term\" with \"long-term\" and \"medium term\" with \"medium-term\"\n",
        "result_df['Sentence'] = result_df['Sentence'].str.replace(r'\\blong term\\b', 'long-term', case=False, regex=True)\n",
        "result_df['Sentence'] = result_df['Sentence'].str.replace(r'\\bmedium term\\b', 'medium-term', case=False, regex=True)\n",
        "\n",
        "# Define a function to lemmatize words and remove stopwords\n",
        "def preprocess_words(words):\n",
        "    lemmatized_words = []\n",
        "    for word in words:\n",
        "        # Lemmatization\n",
        "        doc = nlp(word)\n",
        "        lemmatized_word = doc[0].lemma_.lower()  # Ensure case-insensitive comparison by lowering\n",
        "        # Remove stopwords\n",
        "        if lemmatized_word not in stop_words:\n",
        "            lemmatized_words.append(lemmatized_word)\n",
        "    return lemmatized_words\n",
        "\n",
        "# Define a function to extract spans from sentences for a given label\n",
        "def extract_words_within_spans(df, label):\n",
        "    pattern = r'\\[s\\](.*?)\\[' + label + r'\\]'\n",
        "    spans = df['Sentence'].str.extractall(pattern)\n",
        "    all_words_within_spans = spans[0].str.cat(sep=' ').split()\n",
        "    return preprocess_words(all_words_within_spans)\n",
        "\n",
        "# Step 1: Find the most frequent words in the spans for each label\n",
        "most_frequent_words_in_spans = {}\n",
        "for label in ['tp_h', 'tp_l', 'resp_f', 'resp_s']:\n",
        "    # Extract and preprocess words within spans\n",
        "    words_within_spans = extract_words_within_spans(result_df, label)\n",
        "\n",
        "    # Count word frequencies\n",
        "    word_counts = Counter(words_within_spans)\n",
        "\n",
        "    # Get the most frequent words (e.g., top 10)\n",
        "    most_frequent_words_in_spans[label] = word_counts.most_common(10)\n",
        "\n",
        "    # Print the most frequent words\n",
        "    print(f\"Most frequent words in {label} spans:\")\n",
        "    for word, count in most_frequent_words_in_spans[label]:\n",
        "        print(f\"  - {word}: {count}\")\n",
        "\n",
        "# Save the most frequent words for use in Step 2\n",
        "top_words_dict = most_frequent_words_in_spans\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "293ca30f-caf0-4f51-9ac3-92e960994657",
        "id": "yHTd47Ts2Mo3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "/usr/local/lib/python3.10/dist-packages/spacy/util.py:1740: UserWarning: [W111] Jupyter notebook detected: if using `prefer_gpu()` or `require_gpu()`, include it in the same cell right before `spacy.load()` to ensure that the model is loaded on the correct device. More information: http://spacy.io/usage/v3#jupyter-notebook-gpu\n",
            "  warnings.warn(Warnings.W111)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Most frequent words in tp_h spans:\n",
            "  - need: 21\n",
            "  - increase: 17\n",
            "  - population: 15\n",
            "  - people: 14\n",
            "  - europe: 11\n",
            "  - age: 11\n",
            "  - eu: 11\n",
            "  - migration: 9\n",
            "  - labour: 9\n",
            "  - today: 8\n",
            "Most frequent words in tp_l spans:\n",
            "  - arrival: 44\n",
            "  - irregular: 21\n",
            "  - number: 20\n",
            "  - %: 19\n",
            "  - low: 15\n",
            "  - year: 14\n",
            "  - ,: 13\n",
            "  - level: 12\n",
            "  - unemployment: 11\n",
            "  - compare: 9\n",
            "Most frequent words in resp_f spans:\n",
            "  - need: 22\n",
            "  - european: 13\n",
            "  - member: 11\n",
            "  - state: 11\n",
            "  - soon: 9\n",
            "  - time: 9\n",
            "  - possible: 9\n",
            "  - action: 9\n",
            "  - priority: 9\n",
            "  - proposal: 8\n",
            "Most frequent words in resp_s spans:\n",
            "  - long: 50\n",
            "  - need: 49\n",
            "  - ,: 42\n",
            "  - migration: 38\n",
            "  - policy: 22\n",
            "  - also: 17\n",
            "  - address: 17\n",
            "  - eu: 16\n",
            "  - european: 14\n",
            "  - action: 13\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 2: Calculate the percentage of the most frequent words in the entire dataset\n",
        "\n",
        "percentages = {}\n",
        "for label, top_words in top_words_dict.items():\n",
        "    # Concatenate all sentences and split into words\n",
        "    all_words_in_sentences = ' '.join(result_df['Sentence']).lower().split()\n",
        "\n",
        "    # Preprocess all words in the entire dataset\n",
        "    processed_all_words = preprocess_words(all_words_in_sentences)\n",
        "\n",
        "    # Count occurrences of all words in the dataset\n",
        "    all_words_count = Counter(processed_all_words)\n",
        "\n",
        "    # Calculate the percentage of each top word in the entire dataset\n",
        "    percentages[label] = {}\n",
        "    for word, count_in_spans in top_words:\n",
        "        total_count_in_corpus = all_words_count[word]\n",
        "        percentage = (count_in_spans / total_count_in_corpus * 100) if total_count_in_corpus > 0 else 0\n",
        "        percentages[label][word] = percentage\n",
        "\n",
        "    # Print the percentages\n",
        "    print(f\"Percentage of top words in {label} relative to total occurrences in the corpus:\")\n",
        "    for word, percentage in percentages[label].items():\n",
        "        print(f\"  - {word}: {percentage:.2f}%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "outputId": "b0a97e87-5f42-45bb-a07a-1a802c24a621",
        "id": "w348J6fF3bbb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-16-92480b990555>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0;31m# Preprocess all words in the entire dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0mprocessed_all_words\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpreprocess_words\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_words_in_sentences\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0;31m# Count occurrences of all words in the dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-15-310a9bc4e0fe>\u001b[0m in \u001b[0;36mpreprocess_words\u001b[0;34m(words)\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mword\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mwords\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m         \u001b[0;31m# Lemmatization\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m         \u001b[0mdoc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnlp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     32\u001b[0m         \u001b[0mlemmatized_word\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdoc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlemma_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Ensure case-insensitive comparison by lowering\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m         \u001b[0;31m# Remove stopwords\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/spacy/language.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, text, disable, component_cfg)\u001b[0m\n\u001b[1;32m   1047\u001b[0m                 \u001b[0merror_handler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_error_handler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1048\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1049\u001b[0;31m                 \u001b[0mdoc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdoc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcomponent_cfg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1050\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1051\u001b[0m                 \u001b[0;31m# This typically happens if a component is not initialized\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/spacy/pipeline/trainable_pipe.pyx\u001b[0m in \u001b[0;36mspacy.pipeline.trainable_pipe.TrainablePipe.__call__\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/spacy/pipeline/tok2vec.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, docs)\u001b[0m\n\u001b[1;32m    124\u001b[0m             \u001b[0mwidth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_dim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"nO\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malloc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdoc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdocs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 126\u001b[0;31m         \u001b[0mtokvecs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    127\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mtokvecs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/thinc/layers/concatenate.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mModel\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mInT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOutT\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mInT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_train\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mOutT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCallable\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m     \u001b[0mYs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlayer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_train\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mis_train\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mlayer\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     58\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mYs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m         \u001b[0mdata_l\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackprop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_list_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mYs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/thinc/layers/chain.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(model, X, is_train)\u001b[0m\n\u001b[1;32m     52\u001b[0m     \u001b[0mcallbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mlayer\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m         \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minc_layer_grad\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_train\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mis_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     55\u001b[0m         \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minc_layer_grad\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/thinc/model.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, X, is_train)\u001b[0m\n\u001b[1;32m    308\u001b[0m         \"\"\"Call the model's `forward` function, returning the output and a\n\u001b[1;32m    309\u001b[0m         callback to compute the gradients via backpropagation.\"\"\"\n\u001b[0;32m--> 310\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_train\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mis_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    311\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    312\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minitialize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mInT\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mOutT\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;34m\"Model\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/thinc/layers/hashembed.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(model, ids, is_train)\u001b[0m\n\u001b[1;32m     70\u001b[0m         \u001b[0mseed\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"seed\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m         \u001b[0mkeys\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhash\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mnV\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgather_add\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvectors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m         \u001b[0mdrop_mask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_train\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4) Preparing the prediction sample"
      ],
      "metadata": {
        "id": "dO0GVxj0vz_z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load the translated sentence level data"
      ],
      "metadata": {
        "id": "PIvn_pymwQ7d"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yqLAF9SwwDxa"
      },
      "outputs": [],
      "source": [
        " sentence_df = pd.read_feather('/content/gdrive/My Drive/shuffled_export.feather')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Updated Version with more portfolio information"
      ],
      "metadata": {
        "id": "Y9ZPuy1Wb4Gp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sentence_df = pd.read_feather('/content/gdrive/My Drive/13092024.feather')"
      ],
      "metadata": {
        "id": "06cURadDJXl9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Generate sentence variable that include translated as well as original english sentences"
      ],
      "metadata": {
        "id": "USxQLdEmwjh0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Ensure 'date' column is in datetime format, and extract the year\n",
        "sentence_df['date'] = pd.to_datetime(sentence_df['date'])\n",
        "sentence_df['year'] = sentence_df['date'].dt.year\n",
        "\n",
        "# Filter the DataFrame for rows where 'speaker' is NA (null)\n",
        "na_speaker_df = sentence_df[sentence_df['speaker'].isna()]\n",
        "\n",
        "# Group by 'year' and count the occurrences of NA speakers\n",
        "na_speaker_per_year = na_speaker_df.groupby('year').size()\n",
        "\n",
        "# Group by 'year' and count the total number of entries per year\n",
        "total_per_year = sentence_df.groupby('year').size()\n",
        "\n",
        "# Calculate the percentage of NA speakers per year\n",
        "percentage_na_speakers_per_year = (na_speaker_per_year / total_per_year) * 100\n",
        "\n",
        "# Plot the results\n",
        "plt.figure(figsize=(10, 6))\n",
        "percentage_na_speakers_per_year.plot(kind='bar', color='orange')\n",
        "\n",
        "# Customize the plot\n",
        "plt.title('Percentage of Speaker == NA Over Time by Year', fontsize=14)\n",
        "plt.xlabel('Year', fontsize=12)\n",
        "plt.ylabel('Percentage of NA Speakers', fontsize=12)\n",
        "plt.xticks(rotation=45)\n",
        "plt.grid(True)\n",
        "\n",
        "# Show the plot\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "sWqjWnyooVkH",
        "outputId": "32264a92-ba48-4499-cfdf-8b3f6d7ff386"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bFSV52jFwxCy"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "# Generate the 'sentence' column based on the conditions provided\n",
        "sentence_df['sentence'] = sentence_df.apply(\n",
        "    lambda row: row['sentence_text'] if row['langdetect'] == 'en' else row['sentence_translated'],\n",
        "    axis=1\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the number of speech_id and sentence as well as all variables included in sentence_df\n",
        "print(\"Number of speech_id:\", sentence_df['speech_id'].nunique())\n",
        "print(\"Number of sentences:\", len(sentence_df))\n",
        "print(\"Variables included in subsample_df:\")\n",
        "print(sentence_df.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8c7f4aed-a4db-475e-e971-41071f73214c",
        "id": "bENNcGNyxDFV"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of speech_id: 22625\n",
            "Number of sentences: 1739935\n",
            "Variables included in subsample_df:\n",
            "Index(['speech_id', 'sentence_id', 'date', 'title', 'speaker', 'portfolio',\n",
            "       'context', 'pdfurl', 'url', 'text.annotated', 'after.sampling',\n",
            "       'length', 'langdetect', 'sentence_text', 'title_translated', 'pdfonly',\n",
            "       'probs', 'langdetect_sentence', 'sentence_translated',\n",
            "       'sentence_text_en', 'year', 'sentence'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print a list of unique values in the 'portfolio' column\n",
        "print(sentence_df['portfolio'].unique())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "acd9ce73-ce91-4f4f-8b53-ca711015a651",
        "collapsed": true,
        "id": "Vhvk7mM6xQH9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['President'\n",
            " 'International\\xa0Cooperation, Humanitarian Aid and Crisis Response' ''\n",
            " 'Vice-President  Competition'\n",
            " 'Environment, Maritime Affairs and Fisheries'\n",
            " 'Research, Innovation and Science' 'Crisis Management'\n",
            " 'Vice-President; Euro and Social Dialogue and Financial Stability, Financial Services and Capital Markets Union'\n",
            " 'Cohesion and Reforms'\n",
            " 'Institutional reform, internal market and enterprise'\n",
            " 'European Neighbourhood Policy and Enlargement Negotiations'\n",
            " 'Financial Stability, Financial Services and the Capital Markets Union'\n",
            " 'Environment' 'Internal Market, Industry, Entrepreneurship and SMEs'\n",
            " 'Agriculture and rural development' 'Justice and Home Affairs'\n",
            " 'Migration, Home Affairs and Citizenship'\n",
            " 'Research, Science and Innovation' 'Economy'\n",
            " 'Vice-President  Competition and financial institutions'\n",
            " 'Vice-President; Industry and Entrepreneurship'\n",
            " 'Agriculture and Rural Development' 'Agriculture & Rural Development'\n",
            " 'Vice-President; Commercial Policy and External Relations  with North America, Australasia, East Asia, the OECD and WTO'\n",
            " 'vice-president; Internal Market and Services'\n",
            " 'Industrial affairs, Information & Telecommunications Technologies'\n",
            " 'Health and Food Safety'\n",
            " 'Relations with central and eastern Europe, CFSP and the External Service'\n",
            " 'Vice-President;  Digital Agenda' 'Climate Action'\n",
            " 'External relations and trade policy' 'Development' 'Competition'\n",
            " \"President  Secretariat-General, Legal Service, Security Office, Forward Studies Unit, Inspectorate General,  Joint Interpreting and Conference Service (SCIC), Spokesman's Service, Monetary Matters (with  de Silguy), CFSP (with van den Broek) and Institutional Questions for the 1996 IGC (with Oreja)\"\n",
            " 'Agriculture'\n",
            " 'First Vice-President; Better Regulation, Inter-Institutional Relations, Rule of Law and Charter of Fundamental Rights'\n",
            " 'Employment, Social Affairs and Inclusion'\n",
            " 'Vice-President; Justice, Fundamental Rights and Citizenship'\n",
            " 'Research, Science & Technology  Joint Research Centre, Human Resources, Education, Training and Youth'\n",
            " 'Vice-President; Economic and Monetary Affairs and the Euro'\n",
            " 'Energy, Euratom Supply Agency, SMEs and Tourism'\n",
            " 'Internal Market, Services Customs and Taxation'\n",
            " 'Transport and consumer protection' 'Trade'\n",
            " 'Industry and Entrepreneurship'\n",
            " 'Vice-President;  Economic and Monetary Affairs and the Euro'\n",
            " 'Vice-President  Economic and financial affairs'\n",
            " 'Enlargement and European Neighbourhood Policy'\n",
            " 'Vice-President;  Inter-Institutional Relations and Administration\"'\n",
            " 'Vice-President; Energy Union' 'Climate Action and Energy' 'Enlargement'\n",
            " 'Justice' 'Internal Market' 'Security Union'\n",
            " 'Vice-President  Cooperation, development affairs and enlargement'\n",
            " 'Vice-President; High Representative of the EU for Foreign Affairs and Security Policy'\n",
            " 'Vice-President;  Competition' 'Consumer Policy, Fisheries and ECHO'\n",
            " 'Employment, Social Affairs, Skills and Labour Mobility'\n",
            " 'Agriculture and Rural\\xa0Development'\n",
            " 'Education, Culture, Multilingualism and Youth' 'Transport'\n",
            " 'Justice and Consumers'\n",
            " 'Economic and Financial Affairs, Taxation and Customs'\n",
            " 'Vice-President; Digital Single Market'\n",
            " 'Financial Stability, Financial Services and Capital Markets Union'\n",
            " 'vice-president' 'Trade; An Economy that Works for People'\n",
            " 'Vice-President  External economic affairs and trade policy'\n",
            " 'Internal Market and Services'\n",
            " 'Vice-President  Industry, information technology and science and research'\n",
            " 'Social affairs and employment'\n",
            " 'President; Inter-Institutional Relations and Administration'\n",
            " 'Transport, including TEN' 'Health & Consumer Protection'\n",
            " 'Development & Humanitarian Aid; Social affairs and employment; Health and Consumer Policy'\n",
            " 'Neighbourhood and Enlargement'\n",
            " 'Vice-President  External relations and trade policy'\n",
            " 'Vice-President  Internal market, tax law and customs'\n",
            " 'Vice-President; External Relations  with the Southern Mediterranean, Latin America and the Middle East'\n",
            " 'Vice-President  Agriculture and fisheries' 'Regional Policy'\n",
            " 'Energy, Euratom, small businesses; staff and translation' 'Fisheries'\n",
            " 'Mediterranean policy and north–south relations'\n",
            " 'Vice-President  Cooperation, development and fisheries'\n",
            " 'Mediterranean and Latin American policy'\n",
            " 'Employment & Social Affairs and relations with the EESC'\n",
            " 'Vice-President  Science, research, development, telecommunications and innovation'\n",
            " 'Competition, social affairs and education'\n",
            " 'Vice-President  Internal market and industrial affairs' 'Budget'\n",
            " 'External relations and enlargement'\n",
            " 'Employment, industrial relations and social affairs'\n",
            " 'Taxation and customs union' 'Education & Culture'\n",
            " 'Employment and Social Affairs' 'Economic affairs and employment'\n",
            " 'Vice-President  Science, research, technological development and education'\n",
            " 'Vice-President  Cooperation, development and humanitarian aid'\n",
            " 'Vice-President  Economic & financial affairs and coordination of structural funds'\n",
            " 'Democracy and Demography'\n",
            " 'Relations with African, Caribbean, Pacific Countries,  South Africa and the Lomé Convention'\n",
            " 'Taxation and Customs Union, Audit and Anti-Fraud' 'Energy'\n",
            " 'Home Affairs' 'Development and Humanitarian Aid'\n",
            " 'Vice-President; European Commissioner for Inter-Institutional Relations and Administration, Transport and Energy'\n",
            " 'Health'\n",
            " 'Immigration, Justice & Home Affairs, Financial Control,  Anti-fraud and Relations with the European Ombudsman.'\n",
            " 'Innovation, Research, Culture, Education and Youth; Cohesion and Reforms'\n",
            " 'Vice-President;  Transport'\n",
            " 'Economic & Financial Affairs  Inc. Credit and Investments, the Statistical Office and Monetary Matters'\n",
            " 'External Relations; Neighbourhood Policy'\n",
            " 'Education, Training and Culture'\n",
            " 'Vice-President;  Justice, Freedom and Security'\n",
            " 'Maritime Affairs and Fisheries'\n",
            " 'Vice-President; Jobs, Growth, Investment and Competitiveness'\n",
            " 'Digital Economy and Society' 'Education, Culture, Youth and Sport'\n",
            " 'Interinstitutional Relations and Foresight' 'External Relations'\n",
            " 'First Vice-President; High Representative for Foreign Affairs and Security Policy'\n",
            " 'High Representative for Foreign Affairs and Security Policy'\n",
            " 'Budget and Human Resources; European Commissioner for Digital Economy and Society'\n",
            " 'Information Society and Media' 'Development & Humanitarian Aid'\n",
            " 'International Partnerships'\n",
            " 'Executive vice-president for the European Green deal'\n",
            " 'International Cooperation and Development'\n",
            " 'European Commissioner for Regional Policy' 'Budget and Administration'\n",
            " 'Innovation, Research, Culture, Education and Youth'\n",
            " 'Environment, Oceans and Fisheries'\n",
            " 'Relations with Parliament, culture and audiovisual'\n",
            " 'Competition; A Europe Fit for the Digital Age'\n",
            " 'Enterprise & Information Society' 'Jobs and Social Rights'\n",
            " 'Environment and nuclear security'\n",
            " 'Fisheries and Maritime Affairs; Health and Consumer Policy'\n",
            " 'Equality; Health and Consumer Policy'\n",
            " 'European Commissioner for Regional Policy; European Neighbourhood Policy and Enlargement Negotiations'\n",
            " 'Equality' 'Science and Research'\n",
            " 'First Vice-President;  Institutional Relations and Communication Strategy'\n",
            " 'Health and Consumer Protection' 'Humanitarian Aid and Crisis Management'\n",
            " 'Financial Programming and the Budget'\n",
            " 'Vice-President;  Enterprise and Industry' 'Agriculture and Fisheries'\n",
            " 'Budget, financial control and the cohesion fund' 'Energy & Euratom'\n",
            " 'Taxation, customs union and consumer policies'\n",
            " 'Promoting the European Way of Life'\n",
            " 'Relations with the European Parliament, Culture, Audiovisual Policy,  Relations with the European Parliament, Communication, Information, Openness,  Publications Office and Institutional Questions for the 1996 IGC'\n",
            " 'Research' 'Regional policy and cohesion'\n",
            " 'Environment, nuclear safety and civil protection'\n",
            " 'Institutional reforms, information policy, culture and tourism'\n",
            " 'Audiovisual and cultural affairs' 'Transport and energy'\n",
            " 'Credit, investments, financial instruments and small & medium-sized enterprises'\n",
            " 'Environment, fisheries' 'Economic and Financial Affairs'\n",
            " 'Regional Policy  Inc. Cohesion Fund (with Kinnock & Bjerregaard) and relations with the Committee of the Regions'\n",
            " 'President; Development and Humanitarian Aid'\n",
            " 'President; Industry and Entrepreneurship'\n",
            " 'Budget, Personnel and Administration' 'Fisheries and Maritime Affairs'\n",
            " 'Vice-President; Administrative reform'\n",
            " 'An Economy that Works for People'\n",
            " 'Vice-President; Inter-Institutional Relations and Administration; Health and Consumer Policy'\n",
            " 'High Representative for Foreign Affairs and Security Policy; Energy'\n",
            " 'Vice-President; Inter-Institutional Relations and Administration'\n",
            " 'Economic and Monetary Affairs and the Euro; Vice-President;  Transport'\n",
            " 'Values and Transparency' 'Home Affairs; Cohesion and Reforms'\n",
            " 'Executive vice-president for the European Green deal; Environment, Oceans and Fisheries'\n",
            " 'Vice-President; Jobs, Growth, Investment and Competitiveness; First Vice-President; Better Regulation, Inter-Institutional Relations, Rule of Law and Charter of Fundamental Rights'\n",
            " 'Economic & Monetary Affairs'\n",
            " 'Education, Training and Culture and Multilingualism'\n",
            " 'Health and Consumer Policy'\n",
            " 'Employment, Social\\xa0Affairs and Equal\\xa0Opportunities'\n",
            " 'Multilingualism'\n",
            " 'Economic and Monetary Affairs; Economic and Monetary Affairs and the Euro'\n",
            " 'Economic and Monetary Affairs; Economic and Monetary Affairs and the Euro; Internal Market and Services'\n",
            " 'Employment, Social\\xa0Affairs and Equal\\xa0Opportunities; Internal Market and Services'\n",
            " 'Vice-President  Internal market, industrial affairs and ICT'\n",
            " 'Financial Stability, Financial Services and the Capital Markets Union; Vice-President; External Relations  with the Southern Mediterranean, Latin America and the Middle East'\n",
            " 'European Green Deal'\n",
            " 'Interinstitutional Relations and Foresight; European Green Deal'\n",
            " 'Acting European Commissioner for Digital Economy and Society; Vice-President; Digital Single Market'\n",
            " 'Vice-President; Energy Union; Acting European Commissioner for Digital Single Market'\n",
            " 'Consumer Protection' 'Vice-President; Budget and Human Resources'\n",
            " 'Industry and Entrepreneurship; Internal Market and Services'\n",
            " 'Taxation and Customs Union' 'Vice-President'\n",
            " 'Vice-President; Administrative Affairs, Audit and Anti-Fraud'\n",
            " 'Immigration, Justice & Home Affairs, Financial Control,  Anti-fraud and Relations with the European Ombudsman.; Justice'\n",
            " 'Interinstitutional Relations and Foresight; Health and Food Safety; European Green Deal'\n",
            " 'Energy; Interinstitutional Relations and Foresight; European Green Deal'\n",
            " 'Executive vice-president for the European Green deal; Energy'\n",
            " 'Executive vice-president for the European Green deal; Transport'\n",
            " 'Internal Market; Executive vice-president for the European Green deal; Energy'\n",
            " 'Budget and Human Resources'\n",
            " 'Values and Transparency; Promoting the European Way of Life'\n",
            " 'Vice-President; Jobs, Growth, Investment and Competitiveness; Trade'\n",
            " 'Home Affairs; Promoting the European Way of Life'\n",
            " 'Health and Food Safety; Promoting the European Way of Life'\n",
            " 'Interinstitutional Relations and Foresight; Consumer Protection'\n",
            " 'Democracy and Demography; Home Affairs'\n",
            " 'Research; Financial Stability, Financial Services and Capital Markets Union'\n",
            " 'Executive vice-president for the European Green deal; An Economy that Works for People'\n",
            " 'Jobs and Social Rights; Cohesion and Reforms' 'Energy; Transport'\n",
            " 'Economy; Trade; An Economy that Works for People'\n",
            " 'Jobs and Social Rights; Trade; An Economy that Works for People'\n",
            " 'High Representative for Foreign Affairs and Security Policy; An Economy that Works for People; Trade; Competition; A Europe Fit for the Digital Age'\n",
            " 'Migration, Home Affairs and Citizenship; Employment, Social Affairs, Skills and Labour Mobility'\n",
            " 'President; Digital Economy and Society'\n",
            " 'Immigration, Justice & Home Affairs, Financial Control,  Anti-fraud and Relations with the European Ombudsman.; Migration, Home Affairs and Citizenship'\n",
            " 'President; Internal Market, Industry, Entrepreneurship and SMEs'\n",
            " 'Migration, Home Affairs and Citizenship; Justice and Consumers'\n",
            " 'Crisis Management; Health and Food Safety'\n",
            " 'Education, Culture, Youth and Sport; Migration, Home Affairs and Citizenship'\n",
            " 'Environment, Oceans and Fisheries; Climate Action'\n",
            " 'Internal Market; Competition'\n",
            " 'Internal Market; Trade; An Economy that Works for People'\n",
            " 'Financial Stability, Financial Services and the Capital Markets Union; Trade; An Economy that Works for People'\n",
            " 'Economy; Financial Stability, Financial Services and the Capital Markets Union; Trade; An Economy that Works for People'\n",
            " 'High Representative for Foreign Affairs and Security Policy; Trade; An Economy that Works for People'\n",
            " 'Interinstitutional Relations and Foresight; Consumer Protection; Vice-President; External Relations  with the Southern Mediterranean, Latin America and the Middle East; European Green Deal'\n",
            " 'Executive vice-president for the European Green deal; Economy'\n",
            " 'Executive vice-president for the European Green deal; High Representative for Foreign Affairs and Security Policy'\n",
            " 'Competition; Vice-President; External Relations  with the Southern Mediterranean, Latin America and the Middle East; A Europe Fit for the Digital Age'\n",
            " 'Internal Market; High Representative for Foreign Affairs and Security Policy; Competition'\n",
            " 'Internal Market; High Representative for Foreign Affairs and Security Policy; Competition; A Europe Fit for the Digital Age'\n",
            " 'Migration, Home Affairs and Citizenship; First Vice-President; Better Regulation, Inter-Institutional Relations, Rule of Law and Charter of Fundamental Rights'\n",
            " 'President; Multilingualism'\n",
            " 'President; Vice-President; Jobs, Growth, Investment and Competitiveness'\n",
            " 'Interinstitutional Relations and Foresight; Consumer Protection; Vice-President; External Relations  with the Southern Mediterranean, Latin America and the Middle East'\n",
            " 'Values and Transparency; Justice'\n",
            " 'Vice-President; Jobs, Growth, Investment and Competitiveness; Economic and Financial Affairs, Taxation and Customs'\n",
            " 'Inter-Institutional Relations and Administration'\n",
            " 'President; Migration, Home Affairs and Citizenship'\n",
            " 'Economic & Financial Affairs  Inc. Credit and Investments, the Statistical Office and Monetary Matters; Vice-President; External Relations  with the Southern Mediterranean, Latin America and the Middle East'\n",
            " 'High Representative for Foreign Affairs and Security Policy; International Partnerships'\n",
            " 'Vice-President; External Relations  with the Southern Mediterranean, Latin America and the Middle East; Environment, Maritime Affairs and Fisheries'\n",
            " 'President; Financial Stability, Financial Services and Capital Markets Union'\n",
            " 'Environment, consumer protection and transport'\n",
            " 'Development; Financial Programming and the Budget'\n",
            " 'Justice, Fundamental Rights and Citizenship'\n",
            " 'Vice-President; Energy Union; Acting European Commissioner for Digital Single Market; President'\n",
            " 'President; Interinstitutional Relations and Foresight'\n",
            " 'Vice-President  Cooperation, development affairs and enlargement; Executive vice-president for the European Green deal'\n",
            " 'Consumer Protection; Employment, Social Affairs and Inclusion'\n",
            " 'Executive vice-president for the European Green deal; Energy; Cohesion and Reforms'\n",
            " 'External Relations; Financial Stability, Financial Services and Capital Markets Union'\n",
            " 'External Relations; Home Affairs'\n",
            " 'Vice-President; Jobs, Growth, Investment and Competitiveness; Vice-President; Euro and Social Dialogue and Financial Stability, Financial Services and Capital Markets Union']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(sentence_df['speaker'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "417374a1-977d-4a54-a549-3d70bb636dd1",
        "collapsed": true,
        "id": "-1APjqMXxV7y"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['jean claude juncker' 'kristalina georgieva' 'kadri simson'\n",
            " 'karel van miert' 'karmenu vella' 'phil hogan' 'maire geoghegan quinn'\n",
            " 'janez lenarcic' 'valdis dombrovskis' 'elisa ferreira' None\n",
            " 'vytenis andriukaitis' \"raniero vanni d'archirafi\" 'jacques delors'\n",
            " 'johannes hahn' 'mairead mcguinness' 'janez potocnik'\n",
            " 'elzbieta bienkowska' 'rene steichen' 'antonio vitorino'\n",
            " 'dimitris avramopoulos' 'carlos moedas' 'paolo gentiloni'\n",
            " 'sir leon brittan' 'antonio tajani' 'franz fischler' 'michel barnier'\n",
            " 'martin bangemann' 'stella kyriakides' 'mariya gabriel; marianne thyssen'\n",
            " 'hans van den broek' 'neelie kroes' 'connie hedegaard' 'willy de clercq'\n",
            " 'miguel arias canete' 'andris piebalgs' 'jacques santer'\n",
            " 'christos stylianides' 'jose manuel barroso' 'janusz wojciechowski'\n",
            " 'frans timmermans' 'laszlo andor' 'viviane reding' 'edith cresson'\n",
            " 'olli rehn' 'christos papoutsis' 'mario monti' 'ferdinando nelli feroci'\n",
            " 'jyrki katainen' 'henning christophersen' 'stefan fule' 'maros sefcovic'\n",
            " 'ursula von der leyen' 'adina valean' 'didier reynders' 'thierry breton'\n",
            " 'julian king' 'lorenzo natali' 'federica mogherini' 'joaquin almunia'\n",
            " 'emma bonino' 'marianne thyssen' 'margrethe vestager' 'dacian ciolos'\n",
            " 'androulla vassiliou' 'violeta bulc'\n",
            " 'dimitris avramopoulos; corina cretu' 'vera jourova' 'pierre moscovici'\n",
            " 'andrus ansip' 'jonathan hill' 'gunther oettinger' 'karl heinz narjes'\n",
            " 'padraig flynn' 'neil kinnock' 'david byrne'\n",
            " 'padraig flynn; joe borg; tonio borg' 'oliver varhelyi'\n",
            " 'frans andriessen' 'lord cockfield' 'manuel marin' 'ray macsharry'\n",
            " 'bruce millan' 'antonio cardoso e cunha' 'claude cheysson' 'abel matutes'\n",
            " 'filippo maria pandolfi' 'peter sutherland' 'peter schmidhuber'\n",
            " 'vasso papandreou' 'christiane scrivener' 'anna diamantopoulou'\n",
            " 'romano prodi' 'antonio ruberti' 'dubravka suica' 'joao de deus pinheiro'\n",
            " 'algirdas semeta' 'cecilia malmstrom' 'louis michel' 'loyola de palacio'\n",
            " 'markos kyprianou' 'anita gradin' 'margot wallstrom'\n",
            " 'mariya gabriel; elisa ferreira' 'yves thibault de silguy'\n",
            " 'valdis dombrovskis; vera jourova' 'benita ferrero waldner' 'jan figel'\n",
            " 'franco frattini' 'ylva johansson' 'maria damanaki' 'mariya gabriel'\n",
            " 'tibor navracsics' 'chris patten' 'johannes hahn; jonathan hill'\n",
            " 'catherine ashton' 'charlie mccreevy' 'poul nielson' 'jutta urpilainen'\n",
            " 'neven mimica' 'corina cretu' 'virginijus sinkevicius' 'pascal lamy'\n",
            " 'erkki liikanen' 'nicolas schmit' 'ritt bjerregaard'\n",
            " 'joe borg; tonio borg' 'john dalli; helena dalli' 'frits bolkestein'\n",
            " 'helena dalli' 'iliana ivanova' 'josep borrell' 'dalia grybauskaite'\n",
            " 'danuta hubner' 'gunter verheugen' 'nicolas mosar' 'margaritis schinas'\n",
            " 'marcelino oreja' 'philippe busquin' 'carlo ripa di meana'\n",
            " 'jean dondelinger' 'ioannis paleokrassas' 'monika wulf mathies'\n",
            " 'louis michel; jean claude juncker' 'michel barnier; jean claude juncker'\n",
            " 'joe borg' 'michaele schreyer' 'siim kallas'\n",
            " 'josep borrell; kadri simson' 'elisa ferreira; ylva johansson'\n",
            " 'frans timmermans; virginijus sinkevicius'\n",
            " 'jyrki katainen; frans timmermans' 'pedro solbes' 'jacek dominik'\n",
            " 'jacques barrot' 'stavros dimas' 'janusz lewandowski' 'john dalli'\n",
            " 'louis michel; ursula von der leyen' 'vladimir spidla' 'leonard orban'\n",
            " 'olli rehn; michel barnier' 'karel de gucht' 'mariann fischer boel'\n",
            " 'charlie mccreevy; vladimir spidla' 'wopke hoekstra'\n",
            " 'manuel marin; mairead mcguinness' 'meglena kuneva' 'laszlo kovacs'\n",
            " 'peter mandelson' 'anita gradin; didier reynders'\n",
            " 'maros sefcovic; stella kyriakides' 'maros sefcovic; kadri simson'\n",
            " 'frans timmermans; kadri simson' 'frans timmermans; adina valean'\n",
            " 'frans timmermans; thierry breton; kadri simson'\n",
            " 'vera jourova; margaritis schinas' 'jyrki katainen; cecilia malmstrom'\n",
            " 'margaritis schinas; ylva johansson'\n",
            " 'margaritis schinas; stella kyriakides' 'maros sefcovic; laszlo andor'\n",
            " 'dubravka suica; ylva johansson' 'pavel telicka' 'pawel samecki'\n",
            " 'peter balazs' 'philippe busquin; jonathan hill'\n",
            " 'frans timmermans; valdis dombrovskis' 'nicolas schmit; elisa ferreira'\n",
            " 'adina valean; kadri simson' 'valdis dombrovskis; paolo gentiloni'\n",
            " 'valdis dombrovskis; nicolas schmit'\n",
            " 'valdis dombrovskis; margrethe vestager; josep borrell'\n",
            " 'marianne thyssen; dimitris avramopoulos'\n",
            " 'jean claude juncker; mariya gabriel'\n",
            " 'anita gradin; dimitris avramopoulos'\n",
            " 'jean claude juncker; elzbieta bienkowska'\n",
            " 'vera jourova; dimitris avramopoulos' 'stella kyriakides; janez lenarcic'\n",
            " 'dimitris avramopoulos; tibor navracsics'\n",
            " 'virginijus sinkevicius; wopke hoekstra'\n",
            " 'margrethe vestager; thierry breton' 'valdis dombrovskis; thierry breton'\n",
            " 'valdis dombrovskis; mairead mcguinness'\n",
            " 'valdis dombrovskis; paolo gentiloni; mairead mcguinness'\n",
            " 'valdis dombrovskis; josep borrell'\n",
            " 'manuel marin; maros sefcovic; laszlo andor'\n",
            " 'frans timmermans; paolo gentiloni' 'frans timmermans; josep borrell'\n",
            " 'manuel marin; margrethe vestager'\n",
            " 'margrethe vestager; josep borrell; thierry breton'\n",
            " 'frans timmermans; dimitris avramopoulos'\n",
            " 'leonard orban; ursula von der leyen'\n",
            " 'jyrki katainen; jean claude juncker'\n",
            " 'pierre moscovici; tibor navracsics' 'vera jourova; didier reynders'\n",
            " 'jyrki katainen; pierre moscovici' 'tonio borg'\n",
            " 'jean claude juncker; dimitris avramopoulos'\n",
            " 'manuel marin; yves thibault de silguy' 'josep borrell; jutta urpilainen'\n",
            " 'manuel marin; karmenu vella' 'jean claude juncker; jonathan hill'\n",
            " 'stanley clinton davis' 'maros sefcovic; jean claude juncker'\n",
            " 'maros sefcovic; ursula von der leyen' 'lorenzo natali; frans timmermans'\n",
            " 'sandra kalniete' 'frans timmermans; elisa ferreira; kadri simson'\n",
            " 'chris patten; jonathan hill' 'chris patten; cecilia malmstrom'\n",
            " 'jyrki katainen; valdis dombrovskis']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.1 Selection of relevant speeches"
      ],
      "metadata": {
        "id": "_zeSDWvIxZWO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Ensure 'date' column is in datetime format\n",
        "sentence_df['date'] = pd.to_datetime(sentence_df['date'])\n",
        "\n",
        "# Define the date ranges and names\n",
        "date_range_frattini = (pd.Timestamp('2004-11-22'), pd.Timestamp('2008-05-08'))\n",
        "date_range_barrot = (pd.Timestamp('2008-05-09'), pd.Timestamp('2010-02-09'))\n",
        "\n",
        "# Apply filters\n",
        "subsample_home_df = sentence_df[\n",
        "    (sentence_df['portfolio'].str.contains(\"Home\")) |\n",
        "    ((sentence_df['date'] >= date_range_frattini[0]) & (sentence_df['date'] <= date_range_frattini[1]) & (sentence_df['speaker'] == \"franco frattini\")) |\n",
        "    ((sentence_df['date'] >= date_range_barrot[0]) & (sentence_df['date'] <= date_range_barrot[1]) & (sentence_df['speaker'] == \"jacques barrot\"))\n",
        "]\n",
        "\n",
        "# Display the number of unique speech_id and number of sentences\n",
        "print(\"Number of speech_id:\", subsample_home_df['speech_id'].nunique())\n",
        "print(\"Number of sentences:\", len(subsample_home_df))\n",
        "\n",
        "# Display all variables included in subsample_home_df\n",
        "print(\"Variables included in subsample_home_df:\")\n",
        "print(subsample_home_df.columns)\n",
        "\n",
        "# Print a list of unique values in the 'portfolio' column within the filtered DataFrame\n",
        "print(subsample_home_df['portfolio'].unique())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hVL4dSvPD1JY",
        "outputId": "f0bce92d-dfef-4205-e46c-855d6ee74f29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of speech_id: 878\n",
            "Number of sentences: 58793\n",
            "Variables included in subsample_home_df:\n",
            "Index(['speech_id', 'sentence_id', 'date', 'title', 'speaker', 'portfolio',\n",
            "       'context', 'pdfurl', 'url', 'text.annotated', 'after.sampling',\n",
            "       'length', 'langdetect', 'sentence_text', 'title_translated', 'pdfonly',\n",
            "       'probs', 'langdetect_sentence', 'sentence_translated',\n",
            "       'sentence_text_en', 'year', 'sentence'],\n",
            "      dtype='object')\n",
            "['Justice and Home Affairs' 'Migration, Home Affairs and Citizenship'\n",
            " 'Home Affairs'\n",
            " 'Immigration, Justice & Home Affairs, Financial Control,  Anti-fraud and Relations with the European Ombudsman.'\n",
            " 'Home Affairs; Cohesion and Reforms'\n",
            " 'Vice-President;  Justice, Freedom and Security'\n",
            " 'Immigration, Justice & Home Affairs, Financial Control,  Anti-fraud and Relations with the European Ombudsman.; Justice'\n",
            " 'Home Affairs; Promoting the European Way of Life'\n",
            " 'Democracy and Demography; Home Affairs'\n",
            " 'Migration, Home Affairs and Citizenship; Employment, Social Affairs, Skills and Labour Mobility'\n",
            " 'Immigration, Justice & Home Affairs, Financial Control,  Anti-fraud and Relations with the European Ombudsman.; Migration, Home Affairs and Citizenship'\n",
            " 'Migration, Home Affairs and Citizenship; Justice and Consumers'\n",
            " 'Education, Culture, Youth and Sport; Migration, Home Affairs and Citizenship'\n",
            " 'Migration, Home Affairs and Citizenship; First Vice-President; Better Regulation, Inter-Institutional Relations, Rule of Law and Charter of Fundamental Rights'\n",
            " 'President; Migration, Home Affairs and Citizenship'\n",
            " 'External Relations; Home Affairs']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming your date column is named 'date' and is in datetime format\n",
        "# If not, convert it to datetime format\n",
        "subsample_home_df['date'] = pd.to_datetime(subsample_home_df['date'])\n",
        "\n",
        "# Extract the year from the date column\n",
        "subsample_home_df['year'] = subsample_home_df['date'].dt.year\n",
        "\n",
        "# Group by year and count the number of unique speech_id\n",
        "speech_counts_by_year = subsample_home_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Plot the results\n",
        "plt.figure(figsize=(10, 6))\n",
        "speech_counts_by_year.plot(kind='bar', color='skyblue')\n",
        "\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Number of Home Affairs Commissioner speeches')\n",
        "plt.title('Number of speeches by year')\n",
        "plt.grid(axis='y')\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 830
        },
        "id": "vdDq_Z2Xxcd9",
        "outputId": "bfc1825e-ebdf-4dca-d947-e59e195214a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-15-261a74cbab42>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  subsample_home_df['date'] = pd.to_datetime(subsample_home_df['date'])\n",
            "<ipython-input-15-261a74cbab42>:8: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  subsample_home_df['year'] = subsample_home_df['date'].dt.year\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Display a summary of the date column\n",
        "print(\"Summary of the date column in subsample_home_df:\")\n",
        "print(subsample_home_df['date'].describe())\n",
        "\n",
        "# Display the first few and last few entries of the date column\n",
        "print(\"\\nFirst few entries in the date column:\")\n",
        "print(subsample_home_df['date'].head())\n",
        "\n",
        "print(\"\\nLast few entries in the date column:\")\n",
        "print(subsample_home_df['date'].tail())\n",
        "\n",
        "# Check for any missing or malformed dates\n",
        "missing_dates = subsample_home_df['date'].isnull().sum()\n",
        "print(f\"\\nNumber of missing dates: {missing_dates}\")\n",
        "\n",
        "# Check for any non-datetime entries\n",
        "non_datetime_entries = subsample_home_df[~pd.to_datetime(subsample_home_df['date'], errors='coerce').notna()]\n",
        "if not non_datetime_entries.empty:\n",
        "    print(\"\\nNon-datetime entries in the date column:\")\n",
        "    print(non_datetime_entries)\n",
        "else:\n",
        "    print(\"\\nAll entries in the date column are valid datetime entries.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OFoQCbHx42P9",
        "outputId": "adcf8c7b-4255-495e-dd69-23851195c8c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Summary of the date column in subsample_home_df:\n",
            "count                            58793\n",
            "mean     2014-07-16 02:06:00.900107008\n",
            "min                1995-04-07 00:00:00\n",
            "25%                2008-03-12 00:00:00\n",
            "50%                2016-05-09 00:00:00\n",
            "75%                2021-03-11 00:00:00\n",
            "max                2024-03-12 00:00:00\n",
            "Name: date, dtype: object\n",
            "\n",
            "First few entries in the date column:\n",
            "3250   2003-09-18\n",
            "3251   2003-09-18\n",
            "3252   2003-09-18\n",
            "3253   2003-09-18\n",
            "3254   2003-09-18\n",
            "Name: date, dtype: datetime64[ns]\n",
            "\n",
            "Last few entries in the date column:\n",
            "1739660   2019-04-17\n",
            "1739661   2019-04-17\n",
            "1739662   2019-04-17\n",
            "1739663   2019-04-17\n",
            "1739664   2019-04-17\n",
            "Name: date, dtype: datetime64[ns]\n",
            "\n",
            "Number of missing dates: 0\n",
            "\n",
            "All entries in the date column are valid datetime entries.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "import pandas as pd\n",
        "\n",
        "# Define the regex pattern\n",
        "pattern = re.compile(r'\\b(migration|refugee|asylum)\\b', re.IGNORECASE)\n",
        "\n",
        "# Define a function to check if a sentence matches the pattern\n",
        "def matches_pattern(sentence):\n",
        "    return bool(pattern.search(sentence))\n",
        "\n",
        "# Apply the selection rule to identify speech_ids that meet the criteria\n",
        "selection_rule_1 = subsample_home_df['sentence'].apply(matches_pattern)\n",
        "selected_speech_ids = subsample_home_df[selection_rule_1]['speech_id'].unique()\n",
        "\n",
        "# Filter the DataFrame to include all sentences from the selected speech_ids\n",
        "subsample_home_word_df = subsample_home_df[subsample_home_df['speech_id'].isin(selected_speech_ids)]\n",
        "\n",
        "# Display the number of unique speech_id and sentences, as well as all variables included in subsample_df\n",
        "print(\"Number of unique speech_id:\", subsample_home_word_df['speech_id'].nunique())\n",
        "print(\"Number of sentences:\", len(subsample_home_word_df))\n",
        "print(\"Variables included in subsample_df:\")\n",
        "print(subsample_home_word_df.columns)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nPDLX9xbPsdw",
        "outputId": "88120a38-adfc-4f48-a132-fc890eeb41ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of unique speech_id: 537\n",
            "Number of sentences: 37466\n",
            "Variables included in subsample_df:\n",
            "Index(['speech_id', 'sentence_id', 'date', 'title', 'speaker', 'portfolio',\n",
            "       'context', 'pdfurl', 'url', 'text.annotated', 'after.sampling',\n",
            "       'length', 'langdetect', 'sentence_text', 'title_translated', 'pdfonly',\n",
            "       'probs', 'langdetect_sentence', 'sentence_translated',\n",
            "       'sentence_text_en', 'year', 'sentence'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming your date column is named 'date' and is in datetime format\n",
        "# If not, convert it to datetime format\n",
        "subsample_home_word_df['date'] = pd.to_datetime(subsample_home_word_df['date'])\n",
        "\n",
        "# Extract the year from the date column\n",
        "subsample_home_word_df['year'] = subsample_home_word_df['date'].dt.year\n",
        "\n",
        "# Group by year and count the number of unique speech_id\n",
        "speech_counts_by_year = subsample_home_word_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Plot the results\n",
        "plt.figure(figsize=(10, 6))\n",
        "speech_counts_by_year.plot(kind='bar', color='skyblue')\n",
        "\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Number of migration related Home Affairs Commissioner speeches')\n",
        "plt.title('Number of speeches by year')\n",
        "plt.grid(axis='y')\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 830
        },
        "outputId": "33eb5a7e-8bb4-46cf-a90b-2ece976f585e",
        "id": "_IlTm0zdyJQE"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-18-f8a9f7ddebb5>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  subsample_home_word_df['date'] = pd.to_datetime(subsample_home_word_df['date'])\n",
            "<ipython-input-18-f8a9f7ddebb5>:8: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  subsample_home_word_df['year'] = subsample_home_word_df['date'].dt.year\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Reset the index\n",
        "subsample_home_word_df = subsample_home_word_df.reset_index(drop=True)\n",
        "\n",
        "# Save the selected sample to a new DataFrame or file\n",
        "selected_sample_path = '/content/gdrive/MyDrive/TimePressure/migration_sample_home2.feather'\n",
        "subsample_home_word_df.to_feather(selected_sample_path)"
      ],
      "metadata": {
        "id": "WBTyfVDTP49r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4.2 Selection of President speeches"
      ],
      "metadata": {
        "id": "suhfqgPPSwzX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Select rows where 'portfolio' is equal to \"President\" or 'portfolio' includes the word \"Home\"\n",
        "subsample_president_df = sentence_df[(sentence_df['portfolio'] == \"President\")]\n",
        "\n",
        "# Display the number of speech_id and sentence as well as all variables included in subsample_df\n",
        "print(\"Number of speech_id:\", subsample_president_df['speech_id'].nunique())\n",
        "print(\"Number of sentences:\", len(subsample_president_df))\n",
        "print(\"Variables included in subsample_df:\")\n",
        "print(subsample_president_df.columns)\n",
        "\n",
        "# Print a list of unique values in the 'portfolio' column within the filtered DataFrame\n",
        "print(subsample_president_df['portfolio'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q2nGP7FiPBdO",
        "outputId": "28516a26-8919-4218-c5fe-e5be54747642"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of speech_id: 2298\n",
            "Number of sentences: 180187\n",
            "Variables included in subsample_df:\n",
            "Index(['speech_id', 'sentence_id', 'date', 'title', 'speaker', 'portfolio',\n",
            "       'context', 'pdfurl', 'url', 'text.annotated', 'after.sampling',\n",
            "       'length', 'langdetect', 'sentence_text', 'title_translated', 'pdfonly',\n",
            "       'probs', 'langdetect_sentence', 'sentence_translated',\n",
            "       'sentence_text_en', 'year', 'sentence'],\n",
            "      dtype='object')\n",
            "['President']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming your date column is named 'date' and is in datetime format\n",
        "# If not, convert it to datetime format\n",
        "subsample_president_df['date'] = pd.to_datetime(subsample_president_df['date'])\n",
        "\n",
        "# Extract the year from the date column\n",
        "subsample_president_df['year'] = subsample_president_df['date'].dt.year\n",
        "\n",
        "# Group by year and count the number of unique speech_id\n",
        "speech_counts_by_year = subsample_president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Plot the results\n",
        "plt.figure(figsize=(10, 6))\n",
        "speech_counts_by_year.plot(kind='bar', color='skyblue')\n",
        "\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Number of President speeches')\n",
        "plt.title('Number of speeches by year')\n",
        "plt.grid(axis='y')\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 830
        },
        "id": "1B3Xtxtf0pgg",
        "outputId": "dcbbaa7b-3cc4-48f8-b29b-c0f233191922"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-21-175c9b201f6d>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  subsample_president_df['date'] = pd.to_datetime(subsample_president_df['date'])\n",
            "<ipython-input-21-175c9b201f6d>:8: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  subsample_president_df['year'] = subsample_president_df['date'].dt.year\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "import pandas as pd\n",
        "\n",
        "# Define the regex pattern\n",
        "pattern = re.compile(r'\\b(migration|refugee|asylum)\\b', re.IGNORECASE)\n",
        "\n",
        "# Define a function to check if a sentence matches the pattern\n",
        "def matches_pattern(sentence):\n",
        "    if isinstance(sentence, str):\n",
        "        return bool(pattern.search(sentence))\n",
        "    return False\n",
        "\n",
        "# Apply the selection rule to identify speech_ids that meet the criteria\n",
        "selection_rule_1 = subsample_president_df['sentence'].apply(matches_pattern)\n",
        "selected_speech_ids = subsample_president_df[selection_rule_1]['speech_id'].unique()\n",
        "\n",
        "# Filter the DataFrame to include all sentences from the selected speech_ids\n",
        "subsample_president_word_df = subsample_president_df[subsample_president_df['speech_id'].isin(selected_speech_ids)]\n",
        "\n",
        "# Display the number of unique speech_id and sentences, as well as all variables included in subsample_df\n",
        "print(\"Number of unique speech_id:\", subsample_president_word_df['speech_id'].nunique())\n",
        "print(\"Number of sentences:\", len(subsample_president_word_df))\n",
        "\n",
        "# Optional: Display the first few rows of the selected sample\n",
        "print(subsample_president_word_df.head())\n",
        "\n",
        "# Display the number of speech_id and sentence as well as all variables included in sentence_df\n",
        "print(\"Number of speech_id:\", subsample_president_word_df['speech_id'].nunique())\n",
        "print(\"Number of sentences:\", len(subsample_president_word_df))\n",
        "print(\"Variables included in subsample_president_word_df:\")\n",
        "print(subsample_president_word_df.columns)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F258zVztPWhw",
        "outputId": "53c1a574-3b14-4fe5-bda6-0e49e21d0f7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of unique speech_id: 371\n",
            "Number of sentences: 37423\n",
            "   speech_id  sentence_id       date  \\\n",
            "0          0            1 2017-05-05   \n",
            "1          0            2 2017-05-05   \n",
            "2          0            3 2017-05-05   \n",
            "3          0            4 2017-05-05   \n",
            "4          0            5 2017-05-05   \n",
            "\n",
            "                                               title              speaker  \\\n",
            "0  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "1  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "2  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "3  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "4  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "\n",
            "   portfolio context                                             pdfurl  \\\n",
            "0  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "1  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "2  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "3  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "4  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "\n",
            "                                                 url text.annotated  ...  \\\n",
            "0  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "1  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "2  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "3  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "4  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "\n",
            "   langdetect                                      sentence_text  \\\n",
            "0          fr  Caro Antonio, Cari amici, Monsieur le Ministre...   \n",
            "1          fr  I am hesitating between English and French, bu...   \n",
            "2          fr  And also because the French will have election...   \n",
            "3          fr  Mais avant de faire ça, je dois vous dire que ...   \n",
            "4          fr  et donc je ne peux pas faire un discours magis...   \n",
            "\n",
            "                                    title_translated pdfonly     probs  \\\n",
            "0  ''With an outside view'' – Speech by President...   FALSE  0.785897   \n",
            "1  ''With an outside view'' – Speech by President...   FALSE  0.970456   \n",
            "2  ''With an outside view'' – Speech by President...   FALSE  0.991257   \n",
            "3  ''With an outside view'' – Speech by President...   FALSE  0.995607   \n",
            "4  ''With an outside view'' – Speech by President...   FALSE   0.99236   \n",
            "\n",
            "  langdetect_sentence                                sentence_translated  \\\n",
            "0                  en  Caro Antonio, Cari amici, Mr. Minister, Mr. Ma...   \n",
            "1                  en  I am hesitating between English and French, bu...   \n",
            "2                  en  And also because the French will have election...   \n",
            "3                  fr  But before I do this, I have to tell you that ...   \n",
            "4                  fr  And so I can't make a masterful speech, but I'...   \n",
            "\n",
            "                                    sentence_text_en  year  \\\n",
            "0  Caro Antonio, Cari amici, Mr. Minister, Mr. Ma...  2017   \n",
            "1  I am hesitating between English and French, bu...  2017   \n",
            "2  And also because the French will have election...  2017   \n",
            "3  But before I do this, I have to tell you that ...  2017   \n",
            "4  And so I can't make a masterful speech, but I'...  2017   \n",
            "\n",
            "                                            sentence  \n",
            "0  Caro Antonio, Cari amici, Mr. Minister, Mr. Ma...  \n",
            "1  I am hesitating between English and French, bu...  \n",
            "2  And also because the French will have election...  \n",
            "3  But before I do this, I have to tell you that ...  \n",
            "4  And so I can't make a masterful speech, but I'...  \n",
            "\n",
            "[5 rows x 22 columns]\n",
            "Number of speech_id: 371\n",
            "Number of sentences: 37423\n",
            "Variables included in subsample_president_word_df:\n",
            "Index(['speech_id', 'sentence_id', 'date', 'title', 'speaker', 'portfolio',\n",
            "       'context', 'pdfurl', 'url', 'text.annotated', 'after.sampling',\n",
            "       'length', 'langdetect', 'sentence_text', 'title_translated', 'pdfonly',\n",
            "       'probs', 'langdetect_sentence', 'sentence_translated',\n",
            "       'sentence_text_en', 'year', 'sentence'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming your date column is named 'date' and is in datetime format\n",
        "# If not, convert it to datetime format\n",
        "subsample_president_word_df['date'] = pd.to_datetime(subsample_president_word_df['date'])\n",
        "\n",
        "# Extract the year from the date column\n",
        "subsample_president_word_df['year'] = subsample_president_word_df['date'].dt.year\n",
        "\n",
        "# Group by year and count the number of unique speech_id\n",
        "speech_counts_by_year = subsample_president_word_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Plot the results\n",
        "plt.figure(figsize=(10, 6))\n",
        "speech_counts_by_year.plot(kind='bar', color='skyblue')\n",
        "\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Number of migration rleated President speeches')\n",
        "plt.title('Number of speeches by year')\n",
        "plt.grid(axis='y')\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 830
        },
        "outputId": "f1dad00c-0802-4757-f509-f101be2a3a1b",
        "id": "2y7LfZSSzQcj"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-23-882817f5704b>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  subsample_president_word_df['date'] = pd.to_datetime(subsample_president_word_df['date'])\n",
            "<ipython-input-23-882817f5704b>:8: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  subsample_president_word_df['year'] = subsample_president_word_df['date'].dt.year\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the number of speech_id and sentence as well as all variables included in sentence_df\n",
        "print(\"Number of speech_id:\", subsample_president_word_df['speech_id'].nunique())\n",
        "print(\"Number of sentences:\", len(subsample_president_word_df))\n",
        "print(\"Variables included in subsample_president_word_df:\")\n",
        "print(subsample_president_word_df.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aHkNz32dRYtV",
        "outputId": "a3a70a2d-9614-4371-ea38-f49ac35e413f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of speech_id: 371\n",
            "Number of sentences: 37423\n",
            "Variables included in subsample_president_word_df:\n",
            "Index(['speech_id', 'sentence_id', 'date', 'title', 'speaker', 'portfolio',\n",
            "       'context', 'pdfurl', 'url', 'text.annotated', 'after.sampling',\n",
            "       'length', 'langdetect', 'sentence_text', 'title_translated', 'pdfonly',\n",
            "       'probs', 'langdetect_sentence', 'sentence_translated',\n",
            "       'sentence_text_en', 'year', 'sentence'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Ensure all entries in 'sentence' are strings and handle null values\n",
        "subsample_president_word_df.loc[:, 'sentence'] = subsample_president_word_df['sentence'].apply(lambda x: '' if pd.isnull(x) else str(x))\n",
        "\n",
        "# Verify the conversion\n",
        "print(\"Data type of 'sentence':\", subsample_president_word_df['sentence'].dtype)\n",
        "print(\"Sample data from 'sentence':\")\n",
        "print(subsample_president_word_df['sentence'].head())\n",
        "\n",
        "# Check for non-string items and print their indices (for debugging purposes)\n",
        "non_string_items = subsample_president_word_df[~subsample_president_word_df['sentence'].apply(lambda x: isinstance(x, str))]\n",
        "if not non_string_items.empty:\n",
        "    print(\"Found non-string items at the following indices:\")\n",
        "    print(non_string_items.index.tolist())\n",
        "\n",
        "# Handle non-string items by converting them to strings (again)\n",
        "subsample_president_word_df.loc[:, 'sentence'] = subsample_president_word_df['sentence'].astype(str)\n",
        "\n",
        "# Verify the final conversion\n",
        "print(\"Final data type of 'sentence':\", subsample_president_word_df['sentence'].dtype)\n",
        "print(\"Sample data from 'sentence' after final conversion:\")\n",
        "print(subsample_president_word_df['sentence'].head())\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FQTTxmpHRIYI",
        "outputId": "d9ee1485-9935-4d26-db1f-97b64931ba03"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data type of 'sentence': object\n",
            "Sample data from 'sentence':\n",
            "0    Caro Antonio, Cari amici, Mr. Minister, Mr. Ma...\n",
            "1    I am hesitating between English and French, bu...\n",
            "2    And also because the French will have election...\n",
            "3    But before I do this, I have to tell you that ...\n",
            "4    And so I can't make a masterful speech, but I'...\n",
            "Name: sentence, dtype: object\n",
            "Final data type of 'sentence': object\n",
            "Sample data from 'sentence' after final conversion:\n",
            "0    Caro Antonio, Cari amici, Mr. Minister, Mr. Ma...\n",
            "1    I am hesitating between English and French, bu...\n",
            "2    And also because the French will have election...\n",
            "3    But before I do this, I have to tell you that ...\n",
            "4    And so I can't make a masterful speech, but I'...\n",
            "Name: sentence, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "# Ensure the date column is in datetime format and extract the year\n",
        "for df in [subsample_home_df, subsample_home_word_df, subsample_president_df, subsample_president_word_df]:\n",
        "    df['date'] = pd.to_datetime(df['date'])\n",
        "    df['year'] = df['date'].dt.year\n",
        "\n",
        "# Group by year and count the number of unique speech_id for each subset\n",
        "home_speech_counts_by_year = subsample_home_df.groupby('year')['speech_id'].nunique()\n",
        "home_word_speech_counts_by_year = subsample_home_word_df.groupby('year')['speech_id'].nunique()\n",
        "president_speech_counts_by_year = subsample_president_df.groupby('year')['speech_id'].nunique()\n",
        "president_word_speech_counts_by_year = subsample_president_word_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Create subplots with shared y-axis and same styling\n",
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6), sharey=True)\n",
        "\n",
        "# Plot for Home Affairs Commissioner Speeches\n",
        "ax1.plot(home_speech_counts_by_year.index, home_speech_counts_by_year.values, label='Home Affairs Commissioner Speeches', color='grey', marker='o', linestyle='--')\n",
        "ax1.plot(home_word_speech_counts_by_year.index, home_word_speech_counts_by_year.values, label='Migration-related Home Affairs Commissioner Speeches', color='black', marker='o', linestyle='-')\n",
        "ax1.set_ylabel('Number of Speeches')\n",
        "ax1.legend()\n",
        "ax1.grid(True)\n",
        "\n",
        "# Plot for President Speeches\n",
        "ax2.plot(president_speech_counts_by_year.index, president_speech_counts_by_year.values, label='President Speeches', color='grey', marker='o', linestyle='--')\n",
        "ax2.plot(president_word_speech_counts_by_year.index, president_word_speech_counts_by_year.values, label='Migration-related President Speeches', color='black', marker='o', linestyle='-')\n",
        "ax2.legend()\n",
        "ax2.grid(True)\n",
        "\n",
        "# Set x-axis tick positions and labels for every year\n",
        "for ax in [ax1, ax2]:\n",
        "    ax.set_xticks(range(min(home_speech_counts_by_year.index), max(home_speech_counts_by_year.index) + 1))\n",
        "    ax.set_xticklabels(range(min(home_speech_counts_by_year.index), max(home_speech_counts_by_year.index) + 1), rotation=90)\n",
        "\n",
        "# Ensure both panels have the same style\n",
        "for ax in [ax1, ax2]:\n",
        "    ax.set_facecolor('white')  # Set background color to white\n",
        "    ax.spines['bottom'].set_color('black')  # Set border color\n",
        "    ax.spines['top'].set_color('black')\n",
        "    ax.spines['right'].set_color('black')\n",
        "    ax.spines['left'].set_color('black')\n",
        "\n",
        "# Adjust layout and display the plot\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "zVRDg7bg-bFH",
        "outputId": "b6d7a661-9849-47c3-df4e-faa26f69a6c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-26-7188636c1e5e>:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['date'] = pd.to_datetime(df['date'])\n",
            "<ipython-input-26-7188636c1e5e>:7: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['year'] = df['date'].dt.year\n",
            "<ipython-input-26-7188636c1e5e>:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['date'] = pd.to_datetime(df['date'])\n",
            "<ipython-input-26-7188636c1e5e>:7: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['year'] = df['date'].dt.year\n",
            "<ipython-input-26-7188636c1e5e>:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['date'] = pd.to_datetime(df['date'])\n",
            "<ipython-input-26-7188636c1e5e>:7: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['year'] = df['date'].dt.year\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Reset the index\n",
        "subsample_president_word_df = subsample_president_word_df.reset_index(drop=True)\n",
        "\n",
        "# Save the combined DataFrame to a new file\n",
        "sample_path = '/content/gdrive/MyDrive/TimePressure/migration_sample_president2.feather'\n",
        "subsample_president_word_df.to_feather(sample_path)"
      ],
      "metadata": {
        "id": "DvOF9pqMItg6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4.3 Combined data frame\n",
        "\n",
        "  Including President and Home Affairs speeches"
      ],
      "metadata": {
        "id": "Xf5mHsdBJy16"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Load the existing subsample DataFrames (if necessary)\n",
        "# subsample_president_word_df = pd.read_feather('/content/gdrive/MyDrive/TimePressure/migration_sample_president.feather')\n",
        "# subsample_home_word_df = pd.read_feather('/content/gdrive/MyDrive/TimePressure/migration_sample_home.feather')\n",
        "\n",
        "# Combine both DataFrames\n",
        "combined_df = pd.concat([subsample_president_word_df, subsample_home_word_df])\n",
        "\n",
        "# Reset the index\n",
        "combined_df = combined_df.reset_index(drop=True)\n",
        "\n",
        "# Save the combined DataFrame to a new file\n",
        "combined_sample_path = '/content/gdrive/MyDrive/TimePressure/migration_sample_combined2.feather'\n",
        "combined_df.to_feather(combined_sample_path)\n",
        "\n",
        "# Display the combined DataFrame\n",
        "print(\"Combined DataFrame:\")\n",
        "print(combined_df)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VE0S_TdeIRU3",
        "outputId": "35643417-576b-4bbe-dd28-933fb33ea53b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Combined DataFrame:\n",
            "       speech_id  sentence_id       date  \\\n",
            "0              0            1 2017-05-05   \n",
            "1              0            2 2017-05-05   \n",
            "2              0            3 2017-05-05   \n",
            "3              0            4 2017-05-05   \n",
            "4              0            5 2017-05-05   \n",
            "...          ...          ...        ...   \n",
            "74884      28681           28 2019-04-17   \n",
            "74885      28681           29 2019-04-17   \n",
            "74886      28681           30 2019-04-17   \n",
            "74887      28681           31 2019-04-17   \n",
            "74888      28681           32 2019-04-17   \n",
            "\n",
            "                                                   title  \\\n",
            "0      ''Avec une vue sur l'extérieur'' – Discours du...   \n",
            "1      ''Avec une vue sur l'extérieur'' – Discours du...   \n",
            "2      ''Avec une vue sur l'extérieur'' – Discours du...   \n",
            "3      ''Avec une vue sur l'extérieur'' – Discours du...   \n",
            "4      ''Avec une vue sur l'extérieur'' – Discours du...   \n",
            "...                                                  ...   \n",
            "74884                                               None   \n",
            "74885                                               None   \n",
            "74886                                               None   \n",
            "74887                                               None   \n",
            "74888                                               None   \n",
            "\n",
            "                     speaker                                portfolio context  \\\n",
            "0        jean claude juncker                                President      NA   \n",
            "1        jean claude juncker                                President      NA   \n",
            "2        jean claude juncker                                President      NA   \n",
            "3        jean claude juncker                                President      NA   \n",
            "4        jean claude juncker                                President      NA   \n",
            "...                      ...                                      ...     ...   \n",
            "74884  dimitris avramopoulos  Migration, Home Affairs and Citizenship    None   \n",
            "74885  dimitris avramopoulos  Migration, Home Affairs and Citizenship    None   \n",
            "74886  dimitris avramopoulos  Migration, Home Affairs and Citizenship    None   \n",
            "74887  dimitris avramopoulos  Migration, Home Affairs and Citizenship    None   \n",
            "74888  dimitris avramopoulos  Migration, Home Affairs and Citizenship    None   \n",
            "\n",
            "                                                  pdfurl  \\\n",
            "0      https://ec.europa.eu/commission/presscorner/ap...   \n",
            "1      https://ec.europa.eu/commission/presscorner/ap...   \n",
            "2      https://ec.europa.eu/commission/presscorner/ap...   \n",
            "3      https://ec.europa.eu/commission/presscorner/ap...   \n",
            "4      https://ec.europa.eu/commission/presscorner/ap...   \n",
            "...                                                  ...   \n",
            "74884  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "74885  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "74886  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "74887  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "74888  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "\n",
            "                                                     url text.annotated  ...  \\\n",
            "0      https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "1      https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "2      https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "3      https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "4      https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "...                                                  ...            ...  ...   \n",
            "74884  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "74885  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "74886  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "74887  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "74888  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "\n",
            "       langdetect                                      sentence_text  \\\n",
            "0              fr  Caro Antonio, Cari amici, Monsieur le Ministre...   \n",
            "1              fr  I am hesitating between English and French, bu...   \n",
            "2              fr  And also because the French will have election...   \n",
            "3              fr  Mais avant de faire ça, je dois vous dire que ...   \n",
            "4              fr  et donc je ne peux pas faire un discours magis...   \n",
            "...           ...                                                ...   \n",
            "74884          en  Ahead of the European elections, we need to sh...   \n",
            "74885          en  The European Border and Coast Guard is a case ...   \n",
            "74886          en  A better protection of our external borders is...   \n",
            "74887          en  Today we show how the EU matters and how it ca...   \n",
            "74888          en                                         Thank you.   \n",
            "\n",
            "                                        title_translated pdfonly     probs  \\\n",
            "0      ''With an outside view'' – Speech by President...   FALSE  0.785897   \n",
            "1      ''With an outside view'' – Speech by President...   FALSE  0.970456   \n",
            "2      ''With an outside view'' – Speech by President...   FALSE  0.991257   \n",
            "3      ''With an outside view'' – Speech by President...   FALSE  0.995607   \n",
            "4      ''With an outside view'' – Speech by President...   FALSE   0.99236   \n",
            "...                                                  ...     ...       ...   \n",
            "74884                                               None   FALSE  0.973734   \n",
            "74885                                               None   FALSE   0.90742   \n",
            "74886                                               None   FALSE  0.870993   \n",
            "74887                                               None   FALSE  0.783004   \n",
            "74888                                               None   FALSE  0.496778   \n",
            "\n",
            "      langdetect_sentence                                sentence_translated  \\\n",
            "0                      en  Caro Antonio, Cari amici, Mr. Minister, Mr. Ma...   \n",
            "1                      en  I am hesitating between English and French, bu...   \n",
            "2                      en  And also because the French will have election...   \n",
            "3                      fr  But before I do this, I have to tell you that ...   \n",
            "4                      fr  And so I can't make a masterful speech, but I'...   \n",
            "...                   ...                                                ...   \n",
            "74884                  en                                               None   \n",
            "74885                  en                                               None   \n",
            "74886                  en                                               None   \n",
            "74887                  en                                               None   \n",
            "74888                  en                                               None   \n",
            "\n",
            "                                        sentence_text_en  year  \\\n",
            "0      Caro Antonio, Cari amici, Mr. Minister, Mr. Ma...  2017   \n",
            "1      I am hesitating between English and French, bu...  2017   \n",
            "2      And also because the French will have election...  2017   \n",
            "3      But before I do this, I have to tell you that ...  2017   \n",
            "4      And so I can't make a masterful speech, but I'...  2017   \n",
            "...                                                  ...   ...   \n",
            "74884  Ahead of the European elections, we need to sh...  2019   \n",
            "74885  The European Border and Coast Guard is a case ...  2019   \n",
            "74886  A better protection of our external borders is...  2019   \n",
            "74887  Today we show how the EU matters and how it ca...  2019   \n",
            "74888                                         Thank you.  2019   \n",
            "\n",
            "                                                sentence  \n",
            "0      Caro Antonio, Cari amici, Mr. Minister, Mr. Ma...  \n",
            "1      I am hesitating between English and French, bu...  \n",
            "2      And also because the French will have election...  \n",
            "3      But before I do this, I have to tell you that ...  \n",
            "4      And so I can't make a masterful speech, but I'...  \n",
            "...                                                  ...  \n",
            "74884  Ahead of the European elections, we need to sh...  \n",
            "74885  The European Border and Coast Guard is a case ...  \n",
            "74886  A better protection of our external borders is...  \n",
            "74887  Today we show how the EU matters and how it ca...  \n",
            "74888                                         Thank you.  \n",
            "\n",
            "[74889 rows x 22 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(combined_df.columns)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aL_TTDT4TYuG",
        "outputId": "c7011f77-9266-4f92-92f6-4e458f86b806"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['speech_id', 'sentence_id', 'date', 'title', 'speaker', 'portfolio',\n",
            "       'context', 'pdfurl', 'url', 'text.annotated', 'after.sampling',\n",
            "       'length', 'langdetect', 'sentence_text', 'title_translated', 'pdfonly',\n",
            "       'probs', 'langdetect_sentence', 'sentence_translated',\n",
            "       'sentence_text_en', 'year', 'sentence'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.4) Special: Get speech level data in excel"
      ],
      "metadata": {
        "id": "I02__RKadWqq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Assuming combined_df is your sentence-level DataFrame\n",
        "# Group by 'speech_id' to aggregate sentences into speech-level data\n",
        "speech_level_df = combined_df.groupby('speech_id').agg({\n",
        "    'sentence': ' '.join,  # Concatenate sentences within each speech\n",
        "    'sentence_id': 'count',  # Count number of sentences per speech\n",
        "    # Add other aggregations if necessary, e.g., averaging predictions\n",
        "}).rename(columns={'sentence_id': 'num_sentences'})\n",
        "\n",
        "# Reset index to ensure 'speech_id' is a column in the result\n",
        "speech_level_df = speech_level_df.reset_index()\n",
        "\n",
        "# Save the speech-level DataFrame to an Excel file\n",
        "speech_level_path = '/content/gdrive/MyDrive/TimePressure/migration_sample_speech_level.xlsx'\n",
        "speech_level_df.to_excel(speech_level_path, index=False)\n",
        "\n",
        "print(\"Speech-level data saved to:\", speech_level_path)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "clmoja7Hc2lD",
        "outputId": "84c79b3f-c666-4469-89d1-6b6b15c75339"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Speech-level data saved to: /content/gdrive/MyDrive/TimePressure/migration_sample_speech_level.xlsx\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Group by 'speech_id' to aggregate sentences into speech-level data\n",
        "speech_level_df = combined_df.groupby('speech_id').agg({\n",
        "    'sentence': ' '.join,  # Concatenate sentences into a single speech text\n",
        "    'sentence_id': 'count',  # Count number of sentences per speech\n",
        "    'date': 'first',         # Retain first instance of metadata per speech\n",
        "    'title': 'first',\n",
        "    'speech_id': 'first',\n",
        "    'speaker': 'first',\n",
        "    'portfolio': 'first',\n",
        "    'context': 'first',\n",
        "    'year': 'first'\n",
        "}).rename(columns={'sentence_id': 'num_sentences', 'sentence': 'speech_text'})\n",
        "\n",
        "# Save to Excel\n",
        "speech_level_excel_path = '/content/gdrive/MyDrive/TimePressure/speech_level_data.xlsx'\n",
        "speech_level_df.to_excel(speech_level_excel_path, index=False)\n",
        "\n",
        "print(\"Speech-level data saved to Excel.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D95r5ht2rcaI",
        "outputId": "c1fec724-81e7-45c1-e9b9-12ad63829e23"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Speech-level data saved to Excel.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "40UgUl-pNe_a"
      },
      "source": [
        "# 5) Model a Naive Bayes classifyer as a benchmark\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 1: Load the Excel file into a DataFrame\n",
        "excel_file_path = '/content/gdrive/MyDrive/TimePressure/multilabel_gold_all_final.xlsx'\n",
        "result_df = pd.read_excel(excel_file_path)"
      ],
      "metadata": {
        "id": "5DHvpslexryD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here we simply replace the labels for \"y\" to get the results for all label classes. E.g. tp_l instead of resp_s"
      ],
      "metadata": {
        "id": "GrisPoBG1m_s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.metrics import f1_score\n",
        "\n",
        "# Step 1: Prepare Data\n",
        "X = result_df['Sentence'].astype(str)  # Ensure all features are strings\n",
        "y = result_df['resp_s']  # Labels\n",
        "\n",
        "# Report the prevalence of positive labels in the original dataset\n",
        "positive_label_prevalence = y.sum() / len(y)\n",
        "print(\"Prevalence of positive labels in the original dataset:\", positive_label_prevalence)\n",
        "\n",
        "# Split the data into training and held-out samples with a replicable seed\n",
        "X_train, X_holdout, y_train, y_holdout = train_test_split(X, y, test_size=0.2, random_state=1234)\n",
        "\n",
        "# Report the prevalence of positive labels in the training split\n",
        "positive_label_prevalence_train = y_train.sum() / len(y_train)\n",
        "print(\"Prevalence of positive labels in the training split:\", positive_label_prevalence_train)\n",
        "\n",
        "# Step 2: Train Naive Bayes Classifier\n",
        "# Vectorize the text data\n",
        "vectorizer = CountVectorizer()\n",
        "X_train_vec = vectorizer.fit_transform(X_train)\n",
        "\n",
        "# Initialize and train the classifier\n",
        "nb_classifier = MultinomialNB()\n",
        "nb_classifier.fit(X_train_vec, y_train)\n",
        "\n",
        "# Step 3: Evaluate F1 Score\n",
        "# Vectorize the held-out sample\n",
        "X_holdout_vec = vectorizer.transform(X_holdout)\n",
        "\n",
        "# Predict labels for the held-out sample\n",
        "y_pred = nb_classifier.predict(X_holdout_vec)\n",
        "\n",
        "# Calculate F1 score\n",
        "f1 = f1_score(y_holdout, y_pred)\n",
        "\n",
        "# Report the prevalence of positive labels in the held-out sample\n",
        "positive_label_prevalence_holdout = y_holdout.sum() / len(y_holdout)\n",
        "print(\"Prevalence of positive labels in the held-out sample:\", positive_label_prevalence_holdout)\n",
        "\n",
        "print(\"F1 Score:\", f1)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a7Wy4R_Tu1Lw",
        "outputId": "91f9ada5-5259-4d85-a811-55541b14ada4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prevalence of positive labels in the original dataset: 0.03811871799734497\n",
            "Prevalence of positive labels in the training split: 0.036747273589378855\n",
            "Prevalence of positive labels in the held-out sample: 0.043601895734597156\n",
            "F1 Score: 0.6060606060606061\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 6) Model a SVM classifyer as a benchmark"
      ],
      "metadata": {
        "id": "qts9Ia3mnNZI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6.1) Explicit slow"
      ],
      "metadata": {
        "id": "Tt8SNEs_sDx8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 1: Load the Excel file into a DataFrame\n",
        "excel_file_path = '/content/gdrive/MyDrive/TimePressure/multilabel_gold_all_final.xlsx'\n",
        "result_df = pd.read_excel(excel_file_path)"
      ],
      "metadata": {
        "id": "B5VQpXpN0HPa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "from sklearn.model_selection import StratifiedKFold, cross_validate\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import make_scorer, f1_score, precision_score, recall_score\n",
        "\n",
        "# Set a seed for replicable splits\n",
        "seed_value = 1234\n",
        "\n",
        "# Step 1: Prepare Data\n",
        "# Remove any start and label class annotations\n",
        "def clean_annotations(text):\n",
        "    # Ensure the input is a string, convert if it's not\n",
        "    text = str(text)\n",
        "    return re.sub(r'\\[s\\]|\\[resp_f\\]|\\[tp_h\\]|\\[tp_l\\]', '', text)\n",
        "\n",
        "# Apply the cleaning function to the Sentence column\n",
        "result_df['Cleaned_Sentence'] = result_df['Sentence'].apply(clean_annotations)\n",
        "\n",
        "# Use the cleaned sentence column for modeling\n",
        "X = result_df['Cleaned_Sentence'].astype(str)\n",
        "y = result_df['resp_s']  # Labels\n",
        "\n",
        "# Report the prevalence of positive labels in the original dataset\n",
        "positive_label_prevalence = y.sum() / len(y)\n",
        "print(\"Prevalence of positive labels in the original dataset:\", positive_label_prevalence)\n",
        "\n",
        "# Step 2: Vectorize the text data\n",
        "vectorizer = CountVectorizer()\n",
        "X_vec = vectorizer.fit_transform(X)  # Vectorize the entire dataset\n",
        "\n",
        "# Step 3: Initialize the SVM Classifier with a linear kernel and class weight for imbalance\n",
        "svm_classifier = SVC(kernel='linear', class_weight='balanced', random_state=seed_value)\n",
        "\n",
        "# Step 4: Define the cross-validation strategy\n",
        "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=seed_value)\n",
        "\n",
        "# Step 5: Define the scoring metrics\n",
        "scoring = {\n",
        "    'f1': make_scorer(f1_score),\n",
        "    'precision': make_scorer(precision_score),\n",
        "    'recall': make_scorer(recall_score)\n",
        "}\n",
        "\n",
        "# Step 6: Perform cross-validation\n",
        "cv_results = cross_validate(svm_classifier, X_vec, y, cv=cv, scoring=scoring)\n",
        "\n",
        "# Step 7: Report the cross-validated metrics\n",
        "print(\"Cross-validated F1 scores:\", cv_results['test_f1'])\n",
        "print(\"Average F1 score:\", cv_results['test_f1'].mean())\n",
        "\n",
        "print(\"Cross-validated Precision scores:\", cv_results['test_precision'])\n",
        "print(\"Average Precision score:\", cv_results['test_precision'].mean())\n",
        "\n",
        "print(\"Cross-validated Recall scores:\", cv_results['test_recall'])\n",
        "print(\"Average Recall score:\", cv_results['test_recall'].mean())\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2qwyz5zO2q8q",
        "outputId": "9752ecc0-f0dd-4165-8575-1947742aca58"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prevalence of positive labels in the original dataset: 0.03811871799734497\n",
            "Cross-validated F1 scores: [1.         0.98734177 0.98795181 1.         0.98734177]\n",
            "Average F1 score: 0.9925270703065425\n",
            "Cross-validated Precision scores: [1.         1.         0.97619048 1.         1.        ]\n",
            "Average Precision score: 0.9952380952380953\n",
            "Cross-validated Recall scores: [1.    0.975 1.    1.    0.975]\n",
            "Average Recall score: 0.99\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "\n",
        "# Save both the model and the vectorizer\n",
        "joblib.dump(vectorizer, '/content/gdrive/MyDrive/TimePressure/vectorizer_resp_s.pkl')"
      ],
      "metadata": {
        "id": "KBGZjz3y2Vc5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6.2) Explicit fast"
      ],
      "metadata": {
        "id": "oCE7ZjPp-hgu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 1: Load the Excel file into a DataFrame\n",
        "excel_file_path = '/content/gdrive/MyDrive/TimePressure/multilabel_gold_all_final.xlsx'\n",
        "result_df = pd.read_excel(excel_file_path)"
      ],
      "metadata": {
        "id": "nx_B6s4szTzj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "from sklearn.model_selection import StratifiedKFold, cross_validate\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import make_scorer, f1_score, precision_score, recall_score\n",
        "\n",
        "# Set a seed for replicable splits\n",
        "seed_value = 1234\n",
        "\n",
        "# Step 1: Prepare Data\n",
        "# Remove any start and label class annotations\n",
        "def clean_annotations(text):\n",
        "    # Ensure the input is a string, convert if it's not\n",
        "    text = str(text)\n",
        "    return re.sub(r'\\[s\\]|\\[resp_s\\]|\\[tp_h\\]|\\[tp_l\\]\\[resp_f]', '', text)\n",
        "\n",
        "# Apply the cleaning function to the Sentence column\n",
        "result_df['Cleaned_Sentence'] = result_df['Sentence'].apply(clean_annotations)\n",
        "\n",
        "# Use the cleaned sentence column for modeling\n",
        "X = result_df['Cleaned_Sentence'].astype(str)\n",
        "y = result_df['resp_f']  # Labels\n",
        "\n",
        "# Report the prevalence of positive labels in the original dataset\n",
        "positive_label_prevalence = y.sum() / len(y)\n",
        "print(\"Prevalence of positive labels in the original dataset:\", positive_label_prevalence)\n",
        "\n",
        "# Step 2: Vectorize the text data\n",
        "vectorizer = CountVectorizer()\n",
        "X_vec = vectorizer.fit_transform(X)  # Vectorize the entire dataset\n",
        "\n",
        "# Step 3: Initialize the SVM Classifier with a linear kernel and class weight for imbalance\n",
        "svm_classifier = SVC(kernel='linear', class_weight='balanced', random_state=seed_value)\n",
        "\n",
        "# Step 4: Define the cross-validation strategy\n",
        "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=seed_value)\n",
        "\n",
        "# Step 5: Define the scoring metrics\n",
        "scoring = {\n",
        "    'f1': make_scorer(f1_score),\n",
        "    'precision': make_scorer(precision_score),\n",
        "    'recall': make_scorer(recall_score)\n",
        "}\n",
        "\n",
        "# Step 6: Perform cross-validation\n",
        "cv_results = cross_validate(svm_classifier, X_vec, y, cv=cv, scoring=scoring)\n",
        "\n",
        "# Step 7: Report the cross-validated metrics\n",
        "print(\"Cross-validated F1 scores:\", cv_results['test_f1'])\n",
        "print(\"Average F1 score:\", cv_results['test_f1'].mean())\n",
        "\n",
        "print(\"Cross-validated Precision scores:\", cv_results['test_precision'])\n",
        "print(\"Average Precision score:\", cv_results['test_precision'].mean())\n",
        "\n",
        "print(\"Cross-validated Recall scores:\", cv_results['test_recall'])\n",
        "print(\"Average Recall score:\", cv_results['test_recall'].mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sADow0CdnKi2",
        "outputId": "432d0241-9b8a-4740-8b57-e9ad38da1575"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prevalence of positive labels in the original dataset: 0.033756874644414944\n",
            "Cross-validated F1 scores: [0.98630137 0.97222222 0.98630137 1.         1.        ]\n",
            "Average F1 score: 0.98896499238965\n",
            "Cross-validated Precision scores: [0.97297297 0.97222222 0.97297297 1.         1.        ]\n",
            "Average Precision score: 0.9836336336336335\n",
            "Cross-validated Recall scores: [1.         0.97222222 1.         1.         1.        ]\n",
            "Average Recall score: 0.9944444444444445\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "\n",
        "# Save both the model and the vectorizer\n",
        "joblib.dump(vectorizer, '/content/gdrive/MyDrive/TimePressure/vectorizer_resp_f.pkl')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QkaPvFZ9nKXT",
        "outputId": "84eb562c-816b-4339-c294-4ca75065c317"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/gdrive/MyDrive/TimePressure/vectorizer_resp_f.pkl']"
            ]
          },
          "metadata": {},
          "execution_count": 215
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import re\n",
        "import joblib\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "# Paths\n",
        "model_path = '/content/gdrive/MyDrive/TimePressure/svm_model_resp_f.pkl'\n",
        "vectorizer_path = '/content/gdrive/MyDrive/TimePressure/vectorizer_resp_f.pkl'\n",
        "out_sample_path = '/content/gdrive/MyDrive/TimePressure/migration_sample_combined2.feather'\n",
        "saved_predictions_path = '/content/gdrive/MyDrive/TimePressure/migration_sample_combined_with_predictions.feather'\n",
        "\n",
        "# Step 1: Load Training Data and Verify Vectorizer\n",
        "training_data = pd.read_excel('/content/gdrive/MyDrive/TimePressure/multilabel_gold_all.xlsx')\n",
        "training_data['Cleaned_Sentence'] = training_data['Sentence'].apply(lambda x: re.sub(r'\\[s\\]|\\[resp_f\\]|\\[tp_h\\]|\\[tp_l\\]\\[resp_s]', '', str(x)))\n",
        "\n",
        "# Ensure correct training for consistency\n",
        "X_train = training_data['Cleaned_Sentence'].astype(str)\n",
        "y_train = training_data['resp_f']\n",
        "\n",
        "# Vectorize and train if needed\n",
        "vectorizer = CountVectorizer()\n",
        "X_train_vec = vectorizer.fit_transform(X_train)  # Always fit_transform on training data\n",
        "svm_classifier = SVC(kernel='linear', class_weight='balanced', random_state=1234)\n",
        "svm_classifier.fit(X_train_vec, y_train)\n",
        "\n",
        "# Save model and vectorizer to ensure consistency\n",
        "joblib.dump(svm_classifier, model_path)\n",
        "joblib.dump(vectorizer, vectorizer_path)\n",
        "\n",
        "# Step 2: Load Out-of-Sample Data and Apply Model\n",
        "svm_classifier = joblib.load(model_path)\n",
        "vectorizer = joblib.load(vectorizer_path)\n",
        "\n",
        "# Clean the sentences in the out-of-sample data\n",
        "out_sample_df = pd.read_feather(out_sample_path)\n",
        "out_sample_df['Cleaned_Sentence'] = out_sample_df['sentence'].apply(lambda x: re.sub(r'\\[s\\]|\\[resp_f\\]|\\[tp_h\\]|\\[tp_l\\]\\[resp_s]', '', str(x)))\n",
        "\n",
        "# Vectorize using the loaded vectorizer\n",
        "X_out_sample_vec = vectorizer.transform(out_sample_df['Cleaned_Sentence'].astype(str))\n",
        "\n",
        "# Make predictions\n",
        "out_sample_df['resp_f_pred'] = svm_classifier.predict(X_out_sample_vec)\n",
        "\n",
        "# Save predictions\n",
        "out_sample_df.to_feather(saved_predictions_path)\n",
        "print(\"Predictions added and file saved with predictions.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fcCmUbpgnKFB",
        "outputId": "80ffef0e-6695-48f6-a38b-1c96334742dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predictions added and file saved with predictions.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate prevalence of positive label in predictions\n",
        "positive_label_prevalence = out_sample_df['resp_f_pred'].mean()  # Mean will give proportion of 1s in binary\n",
        "\n",
        "print(f\"Prevalence of positive labels in the predicted out-of-sample data: {positive_label_prevalence:.2%}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GUuW79THnwtC",
        "outputId": "bfba3315-ef9a-43c9-8dea-ddf71dc286ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prevalence of positive labels in the predicted out-of-sample data: 2.02%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6.3) Implicit fast"
      ],
      "metadata": {
        "id": "VpjPSnBQ-loF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 1: Load the Excel file into a DataFrame\n",
        "excel_file_path = '/content/gdrive/MyDrive/TimePressure/multilabel_gold_all_final.xlsx'\n",
        "result_df = pd.read_excel(excel_file_path)"
      ],
      "metadata": {
        "id": "9c0MBbaooKdK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "from sklearn.model_selection import StratifiedKFold, cross_validate\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import make_scorer, f1_score, precision_score, recall_score\n",
        "\n",
        "# Set a seed for replicable splits\n",
        "seed_value = 1234\n",
        "\n",
        "# Step 1: Prepare Data\n",
        "# Remove any start and label class annotations\n",
        "def clean_annotations(text):\n",
        "    # Ensure the input is a string, convert if it's not\n",
        "    text = str(text)\n",
        "    return re.sub(r'\\[s\\]|\\[resp_s\\]|\\[tp_h\\]|\\[tp_l\\]\\[resp_f]', '', text)\n",
        "\n",
        "# Apply the cleaning function to the Sentence column\n",
        "result_df['Cleaned_Sentence'] = result_df['Sentence'].apply(clean_annotations)\n",
        "\n",
        "# Use the cleaned sentence column for modeling\n",
        "X = result_df['Cleaned_Sentence'].astype(str)\n",
        "y = result_df['tp_h']  # Labels\n",
        "\n",
        "# Report the prevalence of positive labels in the original dataset\n",
        "positive_label_prevalence = y.sum() / len(y)\n",
        "print(\"Prevalence of positive labels in the original dataset:\", positive_label_prevalence)\n",
        "\n",
        "# Step 2: Vectorize the text data\n",
        "vectorizer = CountVectorizer()\n",
        "X_vec = vectorizer.fit_transform(X)  # Vectorize the entire dataset\n",
        "\n",
        "# Step 3: Initialize the SVM Classifier with a linear kernel and class weight for imbalance\n",
        "svm_classifier = SVC(kernel='linear', class_weight='balanced', random_state=seed_value)\n",
        "\n",
        "# Step 4: Define the cross-validation strategy\n",
        "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=seed_value)\n",
        "\n",
        "# Step 5: Define the scoring metrics\n",
        "scoring = {\n",
        "    'f1': make_scorer(f1_score),\n",
        "    'precision': make_scorer(precision_score),\n",
        "    'recall': make_scorer(recall_score)\n",
        "}\n",
        "\n",
        "# Step 6: Perform cross-validation\n",
        "cv_results = cross_validate(svm_classifier, X_vec, y, cv=cv, scoring=scoring)\n",
        "\n",
        "# Step 7: Report the cross-validated metrics\n",
        "print(\"Cross-validated F1 scores:\", cv_results['test_f1'])\n",
        "print(\"Average F1 score:\", cv_results['test_f1'].mean())\n",
        "\n",
        "print(\"Cross-validated Precision scores:\", cv_results['test_precision'])\n",
        "print(\"Average Precision score:\", cv_results['test_precision'].mean())\n",
        "\n",
        "print(\"Cross-validated Recall scores:\", cv_results['test_recall'])\n",
        "print(\"Average Recall score:\", cv_results['test_recall'].mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MV8iJ9MgoKRO",
        "outputId": "dcf278f1-6712-42fe-b503-55eb85d1c736"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prevalence of positive labels in the original dataset: 0.0282571591124597\n",
            "Cross-validated F1 scores: [0.21052632 0.29508197 0.14035088 0.25       0.30508475]\n",
            "Average F1 score: 0.24020878119165653\n",
            "Cross-validated Precision scores: [0.22222222 0.29032258 0.14814815 0.25925926 0.31034483]\n",
            "Average Precision score: 0.24605940757219957\n",
            "Cross-validated Recall scores: [0.2        0.3        0.13333333 0.24137931 0.3       ]\n",
            "Average Recall score: 0.2349425287356322\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6.4) Implicit slow"
      ],
      "metadata": {
        "id": "oky9tYaoqMjE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 1: Load the Excel file into a DataFrame\n",
        "excel_file_path = '/content/gdrive/MyDrive/TimePressure/multilabel_gold_all_final.xlsx'\n",
        "result_df = pd.read_excel(excel_file_path)"
      ],
      "metadata": {
        "id": "ImbLrllYqPV3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "from sklearn.model_selection import StratifiedKFold, cross_validate\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import make_scorer, f1_score, precision_score, recall_score\n",
        "\n",
        "# Set a seed for replicable splits\n",
        "seed_value = 1234\n",
        "\n",
        "# Step 1: Prepare Data\n",
        "# Remove any start and label class annotations\n",
        "def clean_annotations(text):\n",
        "    # Ensure the input is a string, convert if it's not\n",
        "    text = str(text)\n",
        "    return re.sub(r'\\[s\\]|\\[resp_s\\]|\\[tp_h\\]|\\[tp_l\\]\\[resp_f]', '', text)\n",
        "\n",
        "# Apply the cleaning function to the Sentence column\n",
        "result_df['Cleaned_Sentence'] = result_df['Sentence'].apply(clean_annotations)\n",
        "\n",
        "# Use the cleaned sentence column for modeling\n",
        "X = result_df['Cleaned_Sentence'].astype(str)\n",
        "y = result_df['tp_l']  # Labels\n",
        "\n",
        "# Report the prevalence of positive labels in the original dataset\n",
        "positive_label_prevalence = y.sum() / len(y)\n",
        "print(\"Prevalence of positive labels in the original dataset:\", positive_label_prevalence)\n",
        "\n",
        "# Step 2: Vectorize the text data\n",
        "vectorizer = CountVectorizer()\n",
        "X_vec = vectorizer.fit_transform(X)  # Vectorize the entire dataset\n",
        "\n",
        "# Step 3: Initialize the SVM Classifier with a linear kernel and class weight for imbalance\n",
        "svm_classifier = SVC(kernel='linear', class_weight='balanced', random_state=seed_value)\n",
        "\n",
        "# Step 4: Define the cross-validation strategy\n",
        "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=seed_value)\n",
        "\n",
        "# Step 5: Define the scoring metrics\n",
        "scoring = {\n",
        "    'f1': make_scorer(f1_score),\n",
        "    'precision': make_scorer(precision_score),\n",
        "    'recall': make_scorer(recall_score)\n",
        "}\n",
        "\n",
        "# Step 6: Perform cross-validation\n",
        "cv_results = cross_validate(svm_classifier, X_vec, y, cv=cv, scoring=scoring)\n",
        "\n",
        "# Step 7: Report the cross-validated metrics\n",
        "print(\"Cross-validated F1 scores:\", cv_results['test_f1'])\n",
        "print(\"Average F1 score:\", cv_results['test_f1'].mean())\n",
        "\n",
        "print(\"Cross-validated Precision scores:\", cv_results['test_precision'])\n",
        "print(\"Average Precision score:\", cv_results['test_precision'].mean())\n",
        "\n",
        "print(\"Cross-validated Recall scores:\", cv_results['test_recall'])\n",
        "print(\"Average Recall score:\", cv_results['test_recall'].mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w8MeivdpqQmJ",
        "outputId": "a2b4e93d-8f60-4bf0-eee9-7a6cd54466e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prevalence of positive labels in the original dataset: 0.01953347240659966\n",
            "Cross-validated F1 scores: [0.97560976 1.         1.         1.         1.        ]\n",
            "Average F1 score: 0.9951219512195122\n",
            "Cross-validated Precision scores: [1. 1. 1. 1. 1.]\n",
            "Average Precision score: 1.0\n",
            "Cross-validated Recall scores: [0.95238095 1.         1.         1.         1.        ]\n",
            "Average Recall score: 0.9904761904761905\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "\n",
        "# Save both the model and the vectorizer\n",
        "joblib.dump(vectorizer, '/content/gdrive/MyDrive/TimePressure/vectorizer_tp_l.pkl')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hdtHsqk9qQY0",
        "outputId": "c0ecf96f-2ade-4831-da3d-584246a8d99f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/gdrive/MyDrive/TimePressure/vectorizer_tp_l.pkl']"
            ]
          },
          "metadata": {},
          "execution_count": 222
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import re\n",
        "import joblib\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "# Paths\n",
        "model_path = '/content/gdrive/MyDrive/TimePressure/svm_model_tp_l.pkl'\n",
        "vectorizer_path = '/content/gdrive/MyDrive/TimePressure/vectorizer_tp_l.pkl'\n",
        "out_sample_path = '/content/gdrive/MyDrive/TimePressure/migration_sample_combined2.feather'\n",
        "saved_predictions_path = '/content/gdrive/MyDrive/TimePressure/migration_sample_combined_with_predictions.feather'\n",
        "\n",
        "# Step 1: Load Training Data and Verify Vectorizer\n",
        "training_data = pd.read_excel('/content/gdrive/MyDrive/TimePressure/multilabel_gold_all.xlsx')\n",
        "training_data['Cleaned_Sentence'] = training_data['Sentence'].apply(lambda x: re.sub(r'\\[s\\]|\\[resp_f\\]|\\[tp_h\\]|\\[tp_l\\]\\[resp_s]', '', str(x)))\n",
        "\n",
        "# Ensure correct training for consistency\n",
        "X_train = training_data['Cleaned_Sentence'].astype(str)\n",
        "y_train = training_data['tp_l']\n",
        "\n",
        "# Vectorize and train if needed\n",
        "vectorizer = CountVectorizer()\n",
        "X_train_vec = vectorizer.fit_transform(X_train)  # Always fit_transform on training data\n",
        "svm_classifier = SVC(kernel='linear', class_weight='balanced', random_state=1234)\n",
        "svm_classifier.fit(X_train_vec, y_train)\n",
        "\n",
        "# Save model and vectorizer to ensure consistency\n",
        "joblib.dump(svm_classifier, model_path)\n",
        "joblib.dump(vectorizer, vectorizer_path)\n",
        "\n",
        "# Step 2: Load Out-of-Sample Data and Apply Model\n",
        "svm_classifier = joblib.load(model_path)\n",
        "vectorizer = joblib.load(vectorizer_path)\n",
        "\n",
        "# Clean the sentences in the out-of-sample data\n",
        "out_sample_df = pd.read_feather(out_sample_path)\n",
        "out_sample_df['Cleaned_Sentence'] = out_sample_df['sentence'].apply(lambda x: re.sub(r'\\[s\\]|\\[resp_f\\]|\\[tp_h\\]|\\[tp_l\\]\\[resp_s]', '', str(x)))\n",
        "\n",
        "# Vectorize using the loaded vectorizer\n",
        "X_out_sample_vec = vectorizer.transform(out_sample_df['Cleaned_Sentence'].astype(str))\n",
        "\n",
        "# Make predictions\n",
        "out_sample_df['tp_l_pred'] = svm_classifier.predict(X_out_sample_vec)\n",
        "\n",
        "# Save predictions\n",
        "out_sample_df.to_feather(saved_predictions_path)\n",
        "print(\"Predictions added and file saved with predictions.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SZK-BhDhqqWZ",
        "outputId": "87547176-37a0-41ef-910f-030769c2612f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predictions added and file saved with predictions.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate prevalence of positive label in predictions\n",
        "positive_label_prevalence = out_sample_df['tp_l_pred'].mean()  # Mean will give proportion of 1s in binary\n",
        "\n",
        "print(f\"Prevalence of positive labels in the predicted out-of-sample data: {positive_label_prevalence:.2%}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g4tUEnXmqqHv",
        "outputId": "6be5bf56-034e-4c8b-cbd1-04bfe9d88fed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prevalence of positive labels in the predicted out-of-sample data: 0.01%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P9DMBB-xb9vf"
      },
      "source": [
        "# 7) Transformers sentence level"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dDBYk5-dNU_r"
      },
      "source": [
        "### 7.1 Preprocessing for the transformers\n",
        "\n",
        "The next step is to load a DistilBERT tokenizer to preprocess the text field:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ghh-jjOkRdal",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "63d613d1-b132-4338-edd0-264049d8af67"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LjPtnSVVRiIy"
      },
      "source": [
        "The tokenizer is a so-called callable and can thus be used like a function: If you input a text string, it return a dictionary with the tokenized text and additional information.\n",
        "\n",
        "The field 'input_ids' indicates the numbers used to represent the tokens in the example sentence.\n",
        "The 'attention_mask' is there to help the model to know to which tokens in of a bunch of sentences it should pay attention when fine-tuning, and which it can ignore.\n",
        "Let's create a helper function that tokenizes the text value of an input called example. This will allow us to iterate over examples in our dataset splits and pre-process them one by one.\n",
        "\n",
        "Note: Setting truncate=True we ensure that none of the text sequences we'll use for fine-tuning is too long for DistilBERT to handle it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BPoVKg1PRl16"
      },
      "outputs": [],
      "source": [
        "# Function to preprocess text using the tokenizer   | change label for other dvs\n",
        "def preprocess_function(row):\n",
        "    encoding = tokenizer(row['cleaned_sentence'], padding=True, truncation=True)\n",
        "    return {\n",
        "        'input_ids': encoding['input_ids'],\n",
        "        'attention_mask': encoding['attention_mask'],\n",
        "        'labels': row['resp_f']\n",
        "    }"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wkjvEibxRoX1"
      },
      "source": [
        "To apply the preprocessing function over the entire dataset, use 🤗 Datasets map function. You can speed up map by setting batched=True to process multiple elements of the dataset at once:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YHtLAMwElSeO"
      },
      "outputs": [],
      "source": [
        "\n",
        "# Use map to apply the preprocess_function to each row\n",
        "tokenized_df = result_df.apply(preprocess_function, axis=1).map(lambda x: {'input_ids': x['input_ids'], 'attention_mask': x['attention_mask'], 'label': x['labels']})\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m8KzlCjg8s0x"
      },
      "outputs": [],
      "source": [
        "# Apply preprocess_function to train_df, dev_df, and test_df\n",
        "train_df = result_df.iloc[train_idxs].apply(preprocess_function, axis=1)\n",
        "dev_df = result_df.iloc[dev_idxs].apply(preprocess_function, axis=1)\n",
        "test_df = result_df.iloc[test_idxs].apply(preprocess_function, axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aVYPS73q8w7k"
      },
      "outputs": [],
      "source": [
        "# Reset indices for train_df, dev_df, and test_df\n",
        "train_df = train_df.reset_index(drop=True)\n",
        "dev_df = dev_df.reset_index(drop=True)\n",
        "test_df = test_df.reset_index(drop=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ka34hpCVZ6_r"
      },
      "outputs": [],
      "source": [
        "# Convert the resulting Series to a list of dictionaries\n",
        "tokenized_data = tokenized_df.tolist()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xgYHJaV8R_vY"
      },
      "source": [
        "### 7.2 Evaluation metric\n",
        "Including a metric during training is often helpful for evaluating your model's performance.\n",
        "\n",
        "Note You could also just load a evaluation method with the 🤗 Evaluate library (see the 🤗 Evaluate quick tour to learn more about how to load and compute a metric)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S7EVhlVDaGa9"
      },
      "outputs": [],
      "source": [
        "def compute_metrics(eval_pred):\n",
        "    predictions, labels = eval_pred\n",
        "    predictions = np.argmax(predictions, axis=1)\n",
        "    p, r, f1, _ = precision_recall_fscore_support(y_true=labels, y_pred=predictions, average='macro', zero_division=0)\n",
        "    # ba = balanced_accuracy_score(y_true=labels, y_pred=predictions)\n",
        "    metrics = {\n",
        "        \"macro_f1\": f1,\n",
        "        \"macro_precision\": p,\n",
        "        \"macro_recall\": r,\n",
        "        # \"balanced_accuracy\": ba\n",
        "    }\n",
        "    return metrics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RZWhQ_8DaQvm"
      },
      "source": [
        "We compute the following metrics:\n",
        "\n",
        "- precision: the share of examples a classifier as correctly assigned into a class\n",
        "- recall: the share of positive examples a classifier labels correctly\n",
        "- F1: a measure combining recall and precision\n",
        "- balanced accuary: an accurarcy metric adjusting for class imbalance\n",
        "\n",
        "<p><a href=\"https://commons.wikimedia.org/wiki/File:Precisionrecall.svg#/media/File:Precisionrecall.svg\"><img src=\"https://upload.wikimedia.org/wikipedia/commons/2/26/Precisionrecall.svg\" alt=\"Precisionrecall.svg\" height=\"800\" width=\"440\"></a><br>By &lt;a href=\"//commons.wikimedia.org/wiki/User:Walber\" title=\"User:Walber\"&gt;Walber&lt;/a&gt; - &lt;span class=\"int-own-work\" lang=\"en\"&gt;Own work&lt;/span&gt;, <a href=\"https://creativecommons.org/licenses/by-sa/4.0\" title=\"Creative Commons Attribution-Share Alike 4.0\">CC BY-SA 4.0</a>, <a href=\"https://commons.wikimedia.org/w/index.php?curid=36926283\">Link</a></p>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FMjB6Ef5aW6d"
      },
      "source": [
        "### 7.3 Training (fine-tuning)\n",
        "\n",
        "Before you start training your model, create two dictionaries that map labels' numeric IDS their character values:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LaTREBbQaSI5"
      },
      "outputs": [],
      "source": [
        "id2label = {0: \"Neutral\", 1: \"High time pressure\"}\n",
        "label2id = {\"Neutral\": 0, \"High time pressure\": 1}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bzlzponhas8m"
      },
      "source": [
        "<Tip>\n",
        "\n",
        "If you aren't familiar with finetuning a model with the [Trainer](https://huggingface.co/docs/transformers/main/en/main_classes/trainer#transformers.Trainer), take a look at the basic tutorial [here](https://huggingface.co/docs/transformers/main/en/tasks/../training#train-with-pytorch-trainer)!\n",
        "\n",
        "</Tip>\n",
        "\n",
        "You're ready to start training your model now! Load DistilBERT with [AutoModelForSequenceClassification](https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.AutoModelForSequenceClassification) along with the number of expected labels, and the label mappings:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86,
          "referenced_widgets": [
            "77b9799a06dd472e841c3c461ffc36f7",
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            "839c2826d9d142a49a9b1fa84c46ca61",
            "e16d39a4f8e64735a12b89f1974691c7",
            "35c2b653cfb240618751a6ce45a8e0cd",
            "4fe43ec5da1d4bf88cdc17e8820f3fc0",
            "0b3b3c98eb9f4dbb938ca8587b44cae7",
            "9cd3d09becdd4e0abd35637751218a4d",
            "3634f440f0f04bffb5ce4a9fc25891a2",
            "f36d06ff446648e1b9df00983c629926"
          ]
        },
        "id": "vqrbFxNCauZe",
        "outputId": "25180a10-d217-4ed2-902f-b2a162a41091"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "77b9799a06dd472e841c3c461ffc36f7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "model = AutoModelForSequenceClassification.from_pretrained(\n",
        "    \"distilbert-base-uncased\", # <== the name of the pre-trained model (downloaded from huggingface hub)\n",
        "    num_labels=2, # number of label classes\n",
        "    id2label=id2label,\n",
        "    label2id=label2id\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KDlSQIZha6pW"
      },
      "source": [
        "To make training as fast as possible, you want to utilize GPU computing.\n",
        "When you run notebooks on Colab, you can enable GPU computing by\n",
        "\n",
        "1. clicking on \"Runtime\" in the menu,\n",
        "2. selecting \"Change runtime type\", and\n",
        "3. choose \"GPU\" in the \"Hardware accelerator\" section of the pop-up\n",
        "\n",
        "If you are running this notebook elsewhere, you want to determine to what kind of device you have access\n",
        "\n",
        "- with a GPU &rarr; \"cuda\"\n",
        "- with MacOS's M1/M2 chip &rarr; \"mps\"\n",
        "- else \"cpu\"\n",
        "\n",
        "We do so like this:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "spQo2ooocI8K"
      },
      "source": [
        "At this point, only three steps remain:\n",
        "\n",
        "1. Define your training hyperparameters in [TrainingArguments](https://huggingface.co/docs/transformers/main/en/main_classes/trainer#transformers.TrainingArguments). The only required parameter is `output_dir` which specifies where to save your model. You'll push this model to the Hub by setting `push_to_hub=True` (you need to be signed in to Hugging Face to upload your model). At the end of each epoch, the [Trainer](https://huggingface.co/docs/transformers/main/en/main_classes/trainer#transformers.Trainer) will evaluate the accuracy and save the training checkpoint.\n",
        "2. Pass the training arguments to [Trainer](https://huggingface.co/docs/transformers/main/en/main_classes/trainer#transformers.Trainer) along with the model, dataset, tokenizer, data collator, and `compute_metrics` function.\n",
        "3. Call [train()](https://huggingface.co/docs/transformers/main/en/main_classes/trainer#transformers.Trainer.train) to finetune your model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hC8TgzomfYwR"
      },
      "outputs": [],
      "source": [
        "model_path = os.path.join(\"/content/gdrive/MyDrive/TimePressure/\", 'distillbert_timepressure')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kWdYVfxRcJn0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ef1cbc64-ef77-49a2-9991-c6b77cb6d90c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=model_path, # leave the following unchanged ;)\n",
        "    learning_rate=2e-5,\n",
        "    per_device_train_batch_size=32,\n",
        "    per_device_eval_batch_size=32,\n",
        "    weight_decay=0.01,\n",
        "    num_train_epochs=5, # <== increase this value to train for longer\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    metric_for_best_model=\"macro_f1\", # <== needs to match one of the names of the dictionary returned by `compute_metrics()` function\n",
        "    save_total_limit=2,\n",
        "    load_best_model_at_end=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_Lj6aBAf4znP"
      },
      "outputs": [],
      "source": [
        "# Define the data collator\n",
        "data_collator = DataCollatorWithPadding(\n",
        "    tokenizer=tokenizer,\n",
        "    max_length=128,\n",
        "    padding='max_length',    # You can adjust this value based on your preference\n",
        "    return_tensors='pt',  # Return PyTorch tensors\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-8aqxliRdB1n"
      },
      "outputs": [],
      "source": [
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_df,  # Use the tokenized data for training\n",
        "    eval_dataset=test_df,\n",
        "    tokenizer=tokenizer,\n",
        "    data_collator=data_collator,\n",
        "    compute_metrics=compute_metrics,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_OGbLgwkdF-7"
      },
      "source": [
        "Now we can finetune the model!\n",
        "\n",
        "**_Warning:_** This will take long if you are using only your CPU 🥹"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "zzPUpDOEdH0F",
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        {
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The `run_name` is currently set to the same value as `TrainingArguments.output_dir`. If this was not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Using wandb-core as the SDK backend. Please refer to https://wandb.me/wandb-core for more information.\n"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
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            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
            "wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:\n"
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            "\u001b[0;31mAbort\u001b[0m                                     Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-92-3435b262f1ae>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m   1936\u001b[0m                 \u001b[0mhf_hub_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_progress_bars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1937\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1938\u001b[0;31m             return inner_training_loop(\n\u001b[0m\u001b[1;32m   1939\u001b[0m                 \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1940\u001b[0m                 \u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   2200\u001b[0m         \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2201\u001b[0m         \u001b[0mgrad_norm\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2202\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2203\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2204\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval_on_start\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer_callback.py\u001b[0m in \u001b[0;36mon_train_begin\u001b[0;34m(self, args, state, control)\u001b[0m\n\u001b[1;32m    458\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mon_train_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTrainingArguments\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTrainerState\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontrol\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTrainerControl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    459\u001b[0m         \u001b[0mcontrol\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_training_stop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 460\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall_event\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"on_train_begin\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontrol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    461\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    462\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mon_train_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTrainingArguments\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTrainerState\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontrol\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTrainerControl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer_callback.py\u001b[0m in \u001b[0;36mcall_event\u001b[0;34m(self, event, args, state, control, **kwargs)\u001b[0m\n\u001b[1;32m    505\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcall_event\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontrol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    506\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mcallback\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 507\u001b[0;31m             result = getattr(callback, event)(\n\u001b[0m\u001b[1;32m    508\u001b[0m                 \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    509\u001b[0m                 \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/integrations/integration_utils.py\u001b[0m in \u001b[0;36mon_train_begin\u001b[0;34m(self, args, state, control, model, **kwargs)\u001b[0m\n\u001b[1;32m    898\u001b[0m             \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    899\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_initialized\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 900\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    901\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    902\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mon_train_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontrol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/integrations/integration_utils.py\u001b[0m in \u001b[0;36msetup\u001b[0;34m(self, args, state, model, **kwargs)\u001b[0m\n\u001b[1;32m    831\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    832\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wandb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 833\u001b[0;31m                 self._wandb.init(\n\u001b[0m\u001b[1;32m    834\u001b[0m                     \u001b[0mproject\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetenv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"WANDB_PROJECT\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"huggingface\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    835\u001b[0m                     \u001b[0;34m**\u001b[0m\u001b[0minit_args\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/wandb/sdk/wandb_init.py\u001b[0m in \u001b[0;36minit\u001b[0;34m(job_type, dir, config, project, entity, reinit, tags, group, name, notes, magic, config_exclude_keys, config_include_keys, anonymous, mode, allow_val_change, resume, force, tensorboard, sync_tensorboard, monitor_gym, save_code, id, fork_from, resume_from, settings)\u001b[0m\n\u001b[1;32m   1268\u001b[0m         \u001b[0;31m# Need to build delay into this sentry capture because our exit hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1269\u001b[0m         \u001b[0;31m# mess with sentry's ability to send out errors before the program ends.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1270\u001b[0;31m         \u001b[0mwandb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sentry\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1271\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mAssertionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# unreachable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/wandb/analytics/sentry.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(self, exc)\u001b[0m\n\u001b[1;32m    159\u001b[0m         \u001b[0;31m# this will messily add this \"reraise\" function to the stack trace,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    160\u001b[0m         \u001b[0;31m# but hopefully it's not too bad\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 161\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0m_safe_noop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/wandb/sdk/wandb_init.py\u001b[0m in \u001b[0;36minit\u001b[0;34m(job_type, dir, config, project, entity, reinit, tags, group, name, notes, magic, config_exclude_keys, config_include_keys, anonymous, mode, allow_val_change, resume, force, tensorboard, sync_tensorboard, monitor_gym, save_code, id, fork_from, resume_from, settings)\u001b[0m\n\u001b[1;32m   1253\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1254\u001b[0m         \u001b[0mwi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_WandbInit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1255\u001b[0;31m         \u001b[0mwi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1256\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1257\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/wandb/sdk/wandb_init.py\u001b[0m in \u001b[0;36msetup\u001b[0;34m(self, kwargs)\u001b[0m\n\u001b[1;32m    303\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    304\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msettings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_offline\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msettings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_noop\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 305\u001b[0;31m             wandb_login._login(\n\u001b[0m\u001b[1;32m    306\u001b[0m                 \u001b[0manonymous\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"anonymous\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    307\u001b[0m                 \u001b[0mforce\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"force\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/wandb/sdk/wandb_login.py\u001b[0m in \u001b[0;36m_login\u001b[0;34m(anonymous, key, relogin, host, force, timeout, _backend, _silent, _disable_warning, _entity)\u001b[0m\n\u001b[1;32m    345\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    346\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 347\u001b[0;31m         \u001b[0mwlogin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprompt_api_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    348\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    349\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwlogin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_key\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/wandb/sdk/wandb_login.py\u001b[0m in \u001b[0;36mprompt_api_key\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    272\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprompt_api_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    273\u001b[0m         \u001b[0;34m\"\"\"Updates the global API key by prompting the user.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 274\u001b[0;31m         \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prompt_api_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    275\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mstatus\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mApiKeyStatus\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNOTTY\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    276\u001b[0m             directive = (\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/wandb/sdk/wandb_login.py\u001b[0m in \u001b[0;36m_prompt_api_key\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    251\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 253\u001b[0;31m                 key = apikey.prompt_api_key(\n\u001b[0m\u001b[1;32m    254\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_settings\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m                     \u001b[0mapi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mapi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/wandb/sdk/lib/apikey.py\u001b[0m in \u001b[0;36mprompt_api_key\u001b[0;34m(settings, api, input_callback, browser_callback, no_offline, no_create, local)\u001b[0m\n\u001b[1;32m    162\u001b[0m                 \u001b[0;34mf\"You can find your API key in your browser here: {app_url}/authorize\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m             )\n\u001b[0;32m--> 164\u001b[0;31m             \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput_callback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapi_ask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    165\u001b[0m         \u001b[0mwrite_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mapi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mapi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mkey\u001b[0m  \u001b[0;31m# type: ignore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/click/termui.py\u001b[0m in \u001b[0;36mprompt\u001b[0;34m(text, default, hide_input, confirmation_prompt, type, value_proc, prompt_suffix, show_default, err, show_choices)\u001b[0m\n\u001b[1;32m    162\u001b[0m     \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 164\u001b[0;31m             \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprompt_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprompt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    165\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/click/termui.py\u001b[0m in \u001b[0;36mprompt_func\u001b[0;34m(text)\u001b[0m\n\u001b[1;32m    145\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhide_input\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    146\u001b[0m                 \u001b[0mecho\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 147\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mAbort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mvalue_proc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAbort\u001b[0m: "
          ]
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "DIlFN9wB_Jsx"
      },
      "outputs": [],
      "source": [
        "# evaluate the final model on the held-out tetst set\n",
        "trainer.evaluate(dev_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Storing the model"
      ],
      "metadata": {
        "id": "M1hgJVei3DPm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming you have trained the model and tokenizer as shown previously\n",
        "model.save_pretrained('/content/gdrive/MyDrive/TimePressure/tp_h_tsl_model/')\n",
        "tokenizer.save_pretrained('/content/gdrive/MyDrive/TimePressure/tp_h_tsl_model/')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5sq1yqP03CpC",
        "outputId": "7bdbf912-1bb9-4707-a3c4-d8f6df99da9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('/content/gdrive/MyDrive/TimePressure/tp_h_tsl_model/tokenizer_config.json',\n",
              " '/content/gdrive/MyDrive/TimePressure/tp_h_tsl_model/special_tokens_map.json',\n",
              " '/content/gdrive/MyDrive/TimePressure/tp_h_tsl_model/vocab.txt',\n",
              " '/content/gdrive/MyDrive/TimePressure/tp_h_tsl_model/added_tokens.json',\n",
              " '/content/gdrive/MyDrive/TimePressure/tp_h_tsl_model/tokenizer.json')"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Test prediction"
      ],
      "metadata": {
        "id": "1tRObRYKKQOU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import torch\n",
        "from transformers import DistilBertTokenizer, DistilBertForSequenceClassification\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Load the trained model and tokenizer\n",
        "model_path = '/content/gdrive/MyDrive/TimePressure/tp_h_tsl_model/'\n",
        "model = DistilBertForSequenceClassification.from_pretrained(model_path)\n",
        "tokenizer = DistilBertTokenizer.from_pretrained(model_path)\n",
        "\n",
        "# Load the selected_rows DataFrame\n",
        "selected_rows = pd.read_csv('selected_rows_with_probabilities.csv')\n",
        "\n",
        "# Ensure 'date' column is in datetime format\n",
        "selected_rows['date'] = pd.to_datetime(selected_rows['date'])\n",
        "\n",
        "# Ensure 'sentence' column is of type string\n",
        "selected_rows['sentence'] = selected_rows['sentence'].astype(str)\n",
        "\n",
        "# Function to predict probabilities\n",
        "def predict_probabilities(model, tokenizer, texts):\n",
        "    model.eval()\n",
        "    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "    model.to(device)\n",
        "\n",
        "    all_probs = []\n",
        "    with torch.no_grad():\n",
        "        for text in texts:\n",
        "            inputs = tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=512).to(device)\n",
        "            outputs = model(**inputs)\n",
        "            probs = torch.nn.functional.softmax(outputs.logits, dim=-1)\n",
        "            all_probs.append(probs[0][1].item())  # Probability of the positive class (tp_h)\n",
        "\n",
        "    return all_probs\n",
        "\n",
        "# Predict probabilities for the 'tp_h' label using the transformer model\n",
        "probabilities_transformer = predict_probabilities(model, tokenizer, selected_rows['sentence'].tolist())\n",
        "\n",
        "# Add the predicted probabilities to the `selected_rows` DataFrame\n",
        "selected_rows['tp_h_probability_transformer'] = probabilities_transformer\n",
        "\n",
        "# Save the updated DataFrame to a file\n",
        "selected_rows.to_csv('selected_rows_with_transformer_probabilities.csv', index=False)\n",
        "\n",
        "# Extract the year from the date column\n",
        "selected_rows['year'] = selected_rows['date'].dt.year\n",
        "\n",
        "# Plot the probability distribution for transformer model\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.hist(selected_rows['tp_h_probability_transformer'], bins=50, color='skyblue', edgecolor='black')\n",
        "plt.title('Probability Distribution of Transformer Predictions')\n",
        "plt.xlabel('Probability')\n",
        "plt.ylabel('Frequency')\n",
        "plt.grid(True)\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Calculate the percentage of sentences with probabilities >0.5 versus <0.5 per year\n",
        "yearly_counts = selected_rows.groupby('year').size()\n",
        "high_prob_counts_transformer = selected_rows[selected_rows['tp_h_probability_transformer'] > 0.5].groupby('year').size()\n",
        "low_prob_counts_transformer = yearly_counts - high_prob_counts_transformer\n",
        "\n",
        "# Calculate the percentage for transformer model\n",
        "percentages_transformer = (high_prob_counts_transformer / yearly_counts) * 100\n",
        "\n",
        "# Plot the percentages for transformer model\n",
        "plt.figure(figsize=(10, 6))\n",
        "percentages_transformer.plot(kind='bar', color='skyblue', edgecolor='black')\n",
        "plt.title('Percentage of Sentences with Transformer Probabilities >0.5 per Year')\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Percentage (%)')\n",
        "plt.xticks(rotation=45)\n",
        "plt.grid(True)\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Optional: If you want to save the plots to files\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.hist(selected_rows['tp_h_probability_transformer'], bins=50, color='skyblue', edgecolor='black')\n",
        "plt.title('Probability Distribution of Transformer Predictions')\n",
        "plt.xlabel('Probability')\n",
        "plt.ylabel('Frequency')\n",
        "plt.grid(True)\n",
        "plt.tight_layout()\n",
        "plt.savefig('probability_distribution_transformer.png')\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "percentages_transformer.plot(kind='bar', color='skyblue', edgecolor='black')\n",
        "plt.title('Percentage of Sentences with Transformer Probabilities >0.5 per Year')\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Percentage (%)')\n",
        "plt.xticks(rotation=45)\n",
        "plt.grid(True)\n",
        "plt.tight_layout()\n",
        "plt.savefig('percentage_per_year_transformer.png')\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "ypiwIZqWKP9V",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "outputId": "2de5cd8f-0a4a-4172-dd14-4134b33d5080"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "[Errno 2] No such file or directory: 'selected_rows_with_probabilities.csv'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-60-fc2452f1bb10>\u001b[0m in \u001b[0;36m<cell line: 12>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;31m# Load the selected_rows DataFrame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mselected_rows\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'selected_rows_with_probabilities.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;31m# Ensure 'date' column is in datetime format\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m    910\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    911\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 912\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    913\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    914\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    575\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    576\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 577\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    578\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1405\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1406\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1407\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1409\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1659\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1660\u001b[0m                     \u001b[0mmode\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\"b\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1661\u001b[0;31m             self.handles = get_handle(\n\u001b[0m\u001b[1;32m   1662\u001b[0m                 \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1663\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    857\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m             \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 859\u001b[0;31m             handle = open(\n\u001b[0m\u001b[1;32m    860\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    861\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'selected_rows_with_probabilities.csv'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I4hDIF82aChe"
      },
      "source": [
        "# 8) Token level Transformers - Implicit time pressure"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HoR1iul4aH4c"
      },
      "source": [
        "### 8.1 Load and pre-process data"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import re\n",
        "from collections import defaultdict\n",
        "import torch\n",
        "from transformers import BertTokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "# Load the Excel file into a DataFrame\n",
        "excel_file_path = '/content/gdrive/MyDrive/TimePressure/multilabel_gold_all_final.xlsx'\n",
        "result_df = pd.read_excel(excel_file_path)\n",
        "\n",
        "# Initialize lists to store token-level data\n",
        "token_list = []\n",
        "label_list = []\n",
        "\n",
        "# Define a function to split a sentence into tokens\n",
        "def tokenize(sentence):\n",
        "    # Split the sentence into tokens (you may need to customize this based on your tokenization requirements)\n",
        "    return sentence.split()\n",
        "\n",
        "# Remove annotation markers for non-classified labels and create token-level data\n",
        "for _, row in result_df.iterrows():\n",
        "    # Convert the Sentence column to a string (if not already)\n",
        "    sentence = str(row['Sentence'])\n",
        "\n",
        "    # Check if tp_h label is not equal to 1\n",
        "    if row['tp_h'] != 1:\n",
        "        # Remove square brackets and their contents from the Sentence column\n",
        "        sentence = re.sub(r'\\[[^\\]]*\\]', '', sentence)\n",
        "\n",
        "    # Split the cleaned sentence into tokens\n",
        "    tokens = tokenize(sentence)\n",
        "\n",
        "    # Initialize a dictionary to store token-level labels\n",
        "    token_labels = defaultdict(int)\n",
        "\n",
        "    # Find the start and end indices of [s] and [tp_h] markers\n",
        "    start_index = sentence.find('[s]')\n",
        "    end_index = sentence.find('[tp_h]')\n",
        "\n",
        "    # Check if both start and end markers are found\n",
        "    if start_index != -1 and end_index != -1:\n",
        "        # Extract the token span between start and end indices\n",
        "        token_span = tokenize(sentence[start_index + len('[s]'):end_index])\n",
        "\n",
        "        # Assign label 1 to tokens within the span, 0 otherwise\n",
        "        for token in token_span:\n",
        "            token_labels[token] = 1\n",
        "\n",
        "    # Append token-label pairs to the lists\n",
        "    for token in tokens:\n",
        "        token_list.append(token)\n",
        "        label_list.append(token_labels[token])\n",
        "\n",
        "# Create a new DataFrame to store token-level data\n",
        "token_df = pd.DataFrame({'Token': token_list, 'Label': label_list})\n",
        "\n",
        "# Save the token-level DataFrame to a new Excel file\n",
        "token_df.to_excel('token_level_data.xlsx', index=False)\n",
        "\n",
        "# Load the token-level DataFrame\n",
        "token_df = pd.read_excel('token_level_data.xlsx')\n",
        "\n",
        "# Tokenizer\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "\n",
        "# Initialize lists to store token-level data and labels\n",
        "tokenized_texts = []\n",
        "labels = []\n",
        "\n",
        "# Iterate through each row of the token-level DataFrame\n",
        "for _, row in token_df.iterrows():\n",
        "    # Tokenize the token using the BERT tokenizer\n",
        "    tokens = tokenizer.tokenize(row['Token'])\n",
        "\n",
        "    # Add special tokens [CLS] and [SEP]\n",
        "    tokens = ['[CLS]'] + tokens + ['[SEP]']\n",
        "\n",
        "    # Add token IDs to the list of token-level data\n",
        "    token_ids = tokenizer.convert_tokens_to_ids(tokens)\n",
        "    tokenized_texts.append(token_ids)\n",
        "\n",
        "    # Add labels to the list of labels\n",
        "    labels.append(row['Label'])\n",
        "\n",
        "# Pad sequences to the maximum length\n",
        "max_len = max(len(seq) for seq in tokenized_texts)\n",
        "input_ids = pad_sequences(tokenized_texts, maxlen=max_len, dtype=\"long\", value=0, truncating=\"post\", padding=\"post\")\n",
        "\n",
        "# Convert lists to PyTorch tensors\n",
        "input_ids = torch.tensor(input_ids)\n",
        "labels = torch.tensor(labels)\n",
        "\n",
        "# Print the first few tokenized texts and labels\n",
        "print(\"Tokenized Texts:\", input_ids[:5])\n",
        "print(\"Labels:\", labels[:5])\n",
        "\n",
        "# Save the preprocessed DataFrame to a new Excel file\n",
        "result_df.to_excel('preprocessed_data.xlsx', index=False)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 612,
          "referenced_widgets": [
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            "a8697a3fdbe8447bb15c5143534f8f34"
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        },
        "id": "EFzGDHrfBfaf",
        "outputId": "e633dd4e-7179-41b7-add4-65685c58e23f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1373fcf25d8a445a8373abd67257728a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f822aa731b9649afac7ef522f14fcd0a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "aa75d881813f46f18842cc50a6e16354"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "438d166a2354425b8c111fd55bd12309"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tokenized Texts: tensor([[  101,  2720,  1010,   102,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0],\n",
            "        [  101,  3472,  1010,   102,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0],\n",
            "        [  101,  6456,  1010,   102,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0],\n",
            "        [  101, 11218,   102,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0],\n",
            "        [  101,  1998,   102,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0]])\n",
            "Labels: tensor([0, 0, 0, 0, 0])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 8.1.2 Run BERT model for implicit high (tp_h)"
      ],
      "metadata": {
        "id": "7t7nBfp_B-w0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from transformers import BertTokenizer, BertForTokenClassification, AdamW, get_linear_schedule_with_warmup\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score\n",
        "from tqdm import tqdm\n",
        "from sklearn.model_selection import train_test_split\n",
        "import numpy as np\n",
        "\n",
        "# Set seeds for replicability\n",
        "seed_value = 1234\n",
        "os.environ['PYTHONHASHSEED'] = str(seed_value)\n",
        "np.random.seed(seed_value)\n",
        "torch.manual_seed(seed_value)\n",
        "torch.cuda.manual_seed(seed_value)\n",
        "torch.cuda.manual_seed_all(seed_value)  # if you are using multi-GPU\n",
        "\n",
        "# Load the token-level DataFrame\n",
        "token_df = pd.read_excel('token_level_data.xlsx')\n",
        "\n",
        "# Tokenizer\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "\n",
        "# Define your token-level dataset\n",
        "class TokenDataset(Dataset):\n",
        "    def __init__(self, tokenizer, token_df, max_length):\n",
        "        self.tokenizer = tokenizer\n",
        "        self.token_df = token_df\n",
        "        self.max_length = max_length\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.token_df)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        tokens = self.tokenizer.tokenize(self.token_df.loc[idx, 'Token'])\n",
        "        tokens = ['[CLS]'] + tokens + ['[SEP]']\n",
        "\n",
        "        input_ids = self.tokenizer.convert_tokens_to_ids(tokens)\n",
        "        input_ids = input_ids[:self.max_length] + [0] * (self.max_length - len(input_ids))\n",
        "\n",
        "        attention_mask = [1] * len(tokens)\n",
        "        attention_mask = attention_mask[:self.max_length] + [0] * (self.max_length - len(attention_mask))\n",
        "\n",
        "        label = self.token_df.loc[idx, 'Label']\n",
        "        label = [label] + [-100] * (self.max_length - 1)  # Pad labels with -100 for ignored tokens\n",
        "\n",
        "        return {\n",
        "            'input_ids': torch.tensor(input_ids),\n",
        "            'attention_mask': torch.tensor(attention_mask),\n",
        "            'labels': torch.tensor(label)\n",
        "        }\n",
        "\n",
        "# Custom collate function to handle padding\n",
        "def custom_collate_fn(batch):\n",
        "    input_ids = torch.stack([item['input_ids'] for item in batch])\n",
        "    attention_masks = torch.stack([item['attention_mask'] for item in batch])\n",
        "    labels = torch.stack([item['labels'] for item in batch])\n",
        "\n",
        "    return input_ids, attention_masks, labels\n",
        "\n",
        "# Calculate class weights\n",
        "class_counts = token_df['Label'].value_counts().to_dict()\n",
        "total_samples = len(token_df)\n",
        "class_weights = {cls: total_samples/count for cls, count in class_counts.items()}\n",
        "\n",
        "weights = [class_weights[cls] for cls in sorted(class_weights.keys())]\n",
        "weights = torch.tensor(weights, dtype=torch.float)\n",
        "\n",
        "# Define your training function\n",
        "def train_model(model, train_loader, val_loader, criterion, optimizer, scheduler, epochs):\n",
        "    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "    model.to(device)\n",
        "    criterion = criterion.to(device)\n",
        "\n",
        "    best_val_f1 = 0\n",
        "\n",
        "    for epoch in range(epochs):\n",
        "        model.train()\n",
        "        total_loss = 0.0\n",
        "\n",
        "        for batch in tqdm(train_loader, desc=f\"Training Epoch {epoch+1}/{epochs}\"):\n",
        "            input_ids, attention_masks, labels = batch\n",
        "            input_ids = input_ids.to(device)\n",
        "            attention_masks = attention_masks.to(device)\n",
        "            labels = labels.to(device)\n",
        "\n",
        "            optimizer.zero_grad()\n",
        "            outputs = model(input_ids=input_ids, attention_mask=attention_masks)\n",
        "            logits = outputs.logits\n",
        "\n",
        "            logits = logits.view(-1, model.config.num_labels)\n",
        "            labels = labels.view(-1)\n",
        "\n",
        "            loss = criterion(logits, labels)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            scheduler.step()\n",
        "\n",
        "            total_loss += loss.item()\n",
        "\n",
        "        avg_loss = total_loss / len(train_loader)\n",
        "        val_precision, val_recall, val_f1 = evaluate_model(model, val_loader, criterion)\n",
        "        print(f\"Epoch {epoch+1}/{epochs}, Loss: {avg_loss}, Validation Precision: {val_precision}, Validation Recall: {val_recall}, Validation F1 Score: {val_f1}\")\n",
        "\n",
        "        # Save model if it has improved\n",
        "        if val_f1 > best_val_f1:\n",
        "            best_val_f1 = val_f1\n",
        "            torch.save(model.state_dict(), 'best_model.pt')\n",
        "\n",
        "def evaluate_model(model, val_loader, criterion):\n",
        "    model.eval()\n",
        "    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "    all_preds = []\n",
        "    all_labels = []\n",
        "    total_loss = 0.0\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for batch in tqdm(val_loader, desc=\"Evaluating\"):\n",
        "            input_ids, attention_masks, labels = batch\n",
        "            input_ids = input_ids.to(device)\n",
        "            attention_masks = attention_masks.to(device)\n",
        "            labels = labels.to(device)\n",
        "\n",
        "            outputs = model(input_ids=input_ids, attention_mask=attention_masks)\n",
        "            logits = outputs.logits\n",
        "\n",
        "            logits = logits.view(-1, model.config.num_labels)\n",
        "            labels = labels.view(-1)\n",
        "\n",
        "            loss = criterion(logits, labels)\n",
        "            total_loss += loss.item()\n",
        "\n",
        "            preds = logits.argmax(dim=-1).cpu().numpy().flatten()\n",
        "            label_ids = labels.cpu().numpy().flatten()\n",
        "\n",
        "            mask = label_ids != -100  # Ignore padded labels\n",
        "            all_preds.extend(preds[mask].tolist())\n",
        "            all_labels.extend(label_ids[mask].tolist())\n",
        "\n",
        "    avg_loss = total_loss / len(val_loader)\n",
        "    precision = precision_score(all_labels, all_preds, average='weighted')\n",
        "    recall = recall_score(all_labels, all_preds, average='weighted')\n",
        "    f1 = f1_score(all_labels, all_preds, average='weighted')\n",
        "    return precision, recall, f1\n",
        "\n",
        "# Split the dataset\n",
        "train_df, temp_df = train_test_split(token_df, test_size=0.3, random_state=seed_value)\n",
        "val_df, test_df = train_test_split(temp_df, test_size=0.5, random_state=seed_value)\n",
        "\n",
        "# Reset indices after splitting\n",
        "train_df = train_df.reset_index(drop=True)\n",
        "val_df = val_df.reset_index(drop=True)\n",
        "test_df = test_df.reset_index(drop=True)\n",
        "\n",
        "# Token-level datasets\n",
        "max_length = 128\n",
        "train_dataset = TokenDataset(tokenizer, train_df, max_length=max_length)\n",
        "val_dataset = TokenDataset(tokenizer, val_df, max_length=max_length)\n",
        "test_dataset = TokenDataset(tokenizer, test_df, max_length=max_length)\n",
        "\n",
        "# DataLoaders for training, validation, and testing\n",
        "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, collate_fn=custom_collate_fn)\n",
        "val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False, collate_fn=custom_collate_fn)\n",
        "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False, collate_fn=custom_collate_fn)\n",
        "\n",
        "# BERT model\n",
        "model = BertForTokenClassification.from_pretrained('bert-base-uncased', num_labels=2)\n",
        "\n",
        "# Define criterion and optimizer\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "criterion = nn.CrossEntropyLoss(weight=weights)  # Use weights calculated earlier\n",
        "optimizer = AdamW(model.parameters(), lr=2e-5, eps=1e-8)\n",
        "scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=0, num_training_steps=len(train_loader)*10)  # Adjust num_training_steps as needed\n",
        "\n",
        "# Define number of epochs\n",
        "epochs = 2\n",
        "\n",
        "# Train the model\n",
        "train_model(model, train_loader, val_loader, criterion, optimizer, scheduler, epochs)\n",
        "\n",
        "# Evaluate the model on test data\n",
        "test_precision, test_recall, test_f1 = evaluate_model(model, test_loader, criterion)\n",
        "print(f\"Test Precision: {test_precision}, Test Recall: {test_recall}, Test F1 Score: {test_f1}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367,
          "referenced_widgets": [
            "9d04a95614034f72856d9305b17aa4f1",
            "a287f6b5ffbb49cb9dace77494fd17a0",
            "e06534f4a01e48a7b11f810419565e14",
            "8a46f85220004f3f8e3a0e711ab32503",
            "5b14eedc31ed4b389f90d411da78a8cb",
            "e266bbdc542c48869aa2678e353e5f40",
            "2a51a08cf1c84d07950c7c854bda665a",
            "d6d6b49ae68846089dda5e3390747716",
            "29f97bd946d041238bced72f17e15346",
            "870e8644513b4cd994cf688dfc88e702",
            "5b63fbf38d1847feb0231958f46bf45f"
          ]
        },
        "id": "BR8teSkBBjtr",
        "outputId": "62c6ea71-7996-4c28-f62c-109ab4ce3ff1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9d04a95614034f72856d9305b17aa4f1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:591: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n",
            "Training Epoch 1/2: 100%|██████████| 2198/2198 [21:59<00:00,  1.67it/s]\n",
            "Evaluating: 100%|██████████| 471/471 [01:36<00:00,  4.89it/s]\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/2, Loss: 0.6319768004071941, Validation Precision: 0.9613652214939393, Validation Recall: 0.9804923362749651, Validation F1 Score: 0.9708345787413\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training Epoch 2/2: 100%|██████████| 2198/2198 [22:09<00:00,  1.65it/s]\n",
            "Evaluating: 100%|██████████| 471/471 [01:34<00:00,  4.98it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 2/2, Loss: 0.6116241422143711, Validation Precision: 0.9674001250773562, Validation Recall: 0.9802269258841484, Validation F1 Score: 0.9710849195903886\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evaluating: 100%|██████████| 471/471 [01:34<00:00,  4.96it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Precision: 0.9715350636706837, Test Recall: 0.9806913940680778, Test F1 Score: 0.9713917356543742\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QQ_WBn6tGMcB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RwPQXzrElLVx"
      },
      "source": [
        "Storing the model for out of sample predicitions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0EIhHBwzlJRu",
        "outputId": "a284b60e-ac47-48a9-f55a-ab6bc9d1bec0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('/content/gdrive/MyDrive/TimePressure/tp_h_model/tokenizer_config.json',\n",
              " '/content/gdrive/MyDrive/TimePressure/tp_h_model/special_tokens_map.json',\n",
              " '/content/gdrive/MyDrive/TimePressure/tp_h_model/vocab.txt',\n",
              " '/content/gdrive/MyDrive/TimePressure/tp_h_model/added_tokens.json')"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "# After training is complete, save the model\n",
        "model.save_pretrained('/content/gdrive/MyDrive/TimePressure/tp_h_model/')\n",
        "\n",
        "# Save the tokenizer\n",
        "tokenizer.save_pretrained('/content/gdrive/MyDrive/TimePressure/tp_h_model/')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yn2rakUGlWmX"
      },
      "source": [
        "## 8.2 Predicting out of sample invocations\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 8.2.1 Prediction in President and Home Affairs speeches"
      ],
      "metadata": {
        "id": "TsbiwxEOdwOL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import torch\n",
        "from transformers import BertTokenizer, BertForTokenClassification\n",
        "from tqdm import tqdm\n",
        "\n",
        "# Define paths\n",
        "model_path = '/content/gdrive/MyDrive/TimePressure/tp_h_model/'\n",
        "prediction_data_path = '/content/gdrive/MyDrive/TimePressure/migration_sample_combined2.feather'\n",
        "\n",
        "# Load the trained model\n",
        "model = BertForTokenClassification.from_pretrained(model_path)\n",
        "model.eval()\n",
        "\n",
        "# Load the tokenizer\n",
        "tokenizer = BertTokenizer.from_pretrained(model_path)\n",
        "\n",
        "# Load the new data\n",
        "prediction_df = pd.read_feather(prediction_data_path)\n",
        "\n",
        "# Define a function to prepare the new data\n",
        "def prepare_data(sentences, tokenizer, max_length):\n",
        "    input_ids = []\n",
        "    attention_masks = []\n",
        "\n",
        "    for sentence in sentences:\n",
        "        tokens = tokenizer.tokenize(sentence)\n",
        "        tokens = ['[CLS]'] + tokens + ['[SEP]']\n",
        "\n",
        "        ids = tokenizer.convert_tokens_to_ids(tokens)\n",
        "        ids = ids[:max_length] + [0] * (max_length - len(ids))\n",
        "\n",
        "        mask = [1] * len(tokens)\n",
        "        mask = mask[:max_length] + [0] * (max_length - len(mask))\n",
        "\n",
        "        input_ids.append(ids)\n",
        "        attention_masks.append(mask)\n",
        "\n",
        "    return torch.tensor(input_ids), torch.tensor(attention_masks)\n",
        "\n",
        "# Prepare for batch processing\n",
        "batch_size = 32\n",
        "max_length = 128\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "model.to(device)\n",
        "\n",
        "predicted_labels = []\n",
        "\n",
        "# Create a progress bar\n",
        "with tqdm(total=len(prediction_df), desc=\"Predicting\", unit=\"sentence\") as pbar:\n",
        "    for i in range(0, len(prediction_df), batch_size):\n",
        "        batch_sentences = prediction_df['sentence'][i:i + batch_size]\n",
        "        input_ids, attention_masks = prepare_data(batch_sentences, tokenizer, max_length)\n",
        "\n",
        "        input_ids = input_ids.to(device)\n",
        "        attention_masks = attention_masks.to(device)\n",
        "\n",
        "        with torch.no_grad():\n",
        "            outputs = model(input_ids=input_ids, attention_mask=attention_masks)\n",
        "            logits = outputs.logits\n",
        "\n",
        "        # Convert logits to probabilities\n",
        "        probs = torch.softmax(logits, dim=-1)\n",
        "\n",
        "        # Convert probabilities to predicted labels\n",
        "        batch_predicted_labels = torch.argmax(probs, dim=-1).cpu().numpy()\n",
        "\n",
        "        predicted_labels.extend(batch_predicted_labels.tolist())\n",
        "\n",
        "        # Update progress bar\n",
        "        pbar.update(len(batch_sentences))\n",
        "\n",
        "# Add the predicted label classes to the DataFrame\n",
        "prediction_df['tp_h_token_pred'] = predicted_labels\n",
        "\n",
        "# Save the updated DataFrame\n",
        "updated_prediction_data_path = '/content/gdrive/MyDrive/TimePressure/migration_sample_with_predictions_h.feather'\n",
        "prediction_df.to_feather(updated_prediction_data_path)\n",
        "\n",
        "# Display the updated DataFrame\n",
        "print(prediction_df.head())\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "collapsed": true,
        "id": "ci_s6rdwL7Uv",
        "outputId": "538df863-aa79-4c08-8c32-336b0ba07d88"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "error",
          "ename": "HFValidationError",
          "evalue": "Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/content/gdrive/MyDrive/TimePressure/tp_h_model/'. Use `repo_type` argument if needed.",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mHFValidationError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/utils/hub.py\u001b[0m in \u001b[0;36mcached_files\u001b[0;34m(path_or_repo_id, filenames, cache_dir, force_download, resume_download, proxies, token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_gated_repo, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash, **deprecated_kwargs)\u001b[0m\n\u001b[1;32m    469\u001b[0m             \u001b[0;31m# This is slightly better for only 1 file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 470\u001b[0;31m             hf_hub_download(\n\u001b[0m\u001b[1;32m    471\u001b[0m                 \u001b[0mpath_or_repo_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    105\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0marg_name\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"repo_id\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"from_id\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"to_id\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 106\u001b[0;31m                 \u001b[0mvalidate_repo_id\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    107\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36mvalidate_repo_id\u001b[0;34m(repo_id)\u001b[0m\n\u001b[1;32m    153\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mrepo_id\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 154\u001b[0;31m         raise HFValidationError(\n\u001b[0m\u001b[1;32m    155\u001b[0m             \u001b[0;34m\"Repo id must be in the form 'repo_name' or 'namespace/repo_name':\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mHFValidationError\u001b[0m: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/content/gdrive/MyDrive/TimePressure/tp_h_model/'. Use `repo_type` argument if needed.",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mHFValidationError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m/tmp/ipython-input-1-896587059.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;31m# Load the trained model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBertForTokenClassification\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/modeling_utils.py\u001b[0m in \u001b[0;36m_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    309\u001b[0m         \u001b[0mold_dtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_default_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 311\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    312\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m             \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_default_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mold_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/modeling_utils.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, weights_only, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m   4471\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPretrainedConfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4472\u001b[0m                 \u001b[0;31m# We make a call to the config file first (which may be absent) to get the commit hash as soon as possible\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4473\u001b[0;31m                 resolved_config_file = cached_file(\n\u001b[0m\u001b[1;32m   4474\u001b[0m                     \u001b[0mpretrained_model_name_or_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4475\u001b[0m                     \u001b[0mCONFIG_NAME\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/utils/hub.py\u001b[0m in \u001b[0;36mcached_file\u001b[0;34m(path_or_repo_id, filename, **kwargs)\u001b[0m\n\u001b[1;32m    310\u001b[0m     \u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    311\u001b[0m     \"\"\"\n\u001b[0;32m--> 312\u001b[0;31m     \u001b[0mfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcached_files\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_or_repo_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpath_or_repo_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilenames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    313\u001b[0m     \u001b[0mfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfile\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    314\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/utils/hub.py\u001b[0m in \u001b[0;36mcached_files\u001b[0;34m(path_or_repo_id, filenames, cache_dir, force_download, resume_download, proxies, token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_gated_repo, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash, **deprecated_kwargs)\u001b[0m\n\u001b[1;32m    520\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    521\u001b[0m         \u001b[0;31m# Now we try to recover if we can find all files correctly in the cache\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 522\u001b[0;31m         resolved_files = [\n\u001b[0m\u001b[1;32m    523\u001b[0m             \u001b[0m_get_cache_file_to_return\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_or_repo_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcache_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrevision\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfull_filenames\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    524\u001b[0m         ]\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/utils/hub.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    521\u001b[0m         \u001b[0;31m# Now we try to recover if we can find all files correctly in the cache\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    522\u001b[0m         resolved_files = [\n\u001b[0;32m--> 523\u001b[0;31m             \u001b[0m_get_cache_file_to_return\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_or_repo_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcache_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrevision\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfull_filenames\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    524\u001b[0m         ]\n\u001b[1;32m    525\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfile\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mresolved_files\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/utils/hub.py\u001b[0m in \u001b[0;36m_get_cache_file_to_return\u001b[0;34m(path_or_repo_id, full_filename, cache_dir, revision)\u001b[0m\n\u001b[1;32m    138\u001b[0m ):\n\u001b[1;32m    139\u001b[0m     \u001b[0;31m# We try to see if we have a cached version (not up to date):\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 140\u001b[0;31m     \u001b[0mresolved_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtry_to_load_from_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_or_repo_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfull_filename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcache_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcache_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrevision\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrevision\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    141\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mresolved_file\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mresolved_file\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0m_CACHED_NO_EXIST\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresolved_file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    104\u001b[0m         ):\n\u001b[1;32m    105\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0marg_name\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"repo_id\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"from_id\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"to_id\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 106\u001b[0;31m                 \u001b[0mvalidate_repo_id\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    107\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0marg_name\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"token\"\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0marg_value\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36mvalidate_repo_id\u001b[0;34m(repo_id)\u001b[0m\n\u001b[1;32m    152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mrepo_id\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 154\u001b[0;31m         raise HFValidationError(\n\u001b[0m\u001b[1;32m    155\u001b[0m             \u001b[0;34m\"Repo id must be in the form 'repo_name' or 'namespace/repo_name':\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    156\u001b[0m             \u001b[0;34mf\" '{repo_id}'. Use `repo_type` argument if needed.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mHFValidationError\u001b[0m: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/content/gdrive/MyDrive/TimePressure/tp_h_model/'. Use `repo_type` argument if needed."
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 8.2.2 Aggregation to sentence level predictions"
      ],
      "metadata": {
        "id": "W8ujtDw0CegJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Load your DataFrame containing token-level predictions\n",
        "# Assume you have a DataFrame with original sentences and their token-level predictions\n",
        "# prediction_df: DataFrame containing original sentences and their token-level predictions\n",
        "\n",
        "# Example column names\n",
        "# 'sentence' - original sentence\n",
        "# 'tp_h_token_pred' - token-level predictions\n",
        "\n",
        "# Create new columns for sentence-level predictions\n",
        "prediction_df['tp_h_sentence_pred_majority'] = None\n",
        "prediction_df['tp_h_sentence_pred_first_non_negative'] = None\n",
        "\n",
        "# Initialize lists to hold sentence-level predictions\n",
        "sentence_predictions_majority = []\n",
        "sentence_predictions_first_non_negative = []\n",
        "\n",
        "# Iterate through each unique sentence in the DataFrame\n",
        "for sentence in prediction_df['sentence'].unique():\n",
        "    # Get token-level predictions for the current sentence\n",
        "    token_predictions = prediction_df[prediction_df['sentence'] == sentence]['tp_h_token_pred'].tolist()\n",
        "\n",
        "    # If the predictions are lists, flatten them\n",
        "    token_predictions = [item for sublist in token_predictions for item in sublist]  # Flattening\n",
        "\n",
        "    # Filter out ignored tokens (e.g., -100)\n",
        "    valid_predictions = [pred for pred in token_predictions if pred != -100]\n",
        "\n",
        "    # Majority vote aggregation\n",
        "    if valid_predictions:\n",
        "        # Use the most common valid prediction\n",
        "        sentence_pred_majority = max(set(valid_predictions), key=valid_predictions.count)  # Majority vote\n",
        "    else:\n",
        "        sentence_pred_majority = -100  # Default value if no valid predictions\n",
        "\n",
        "    # First non-negative label aggregation\n",
        "    sentence_pred_first_non_negative = next((pred for pred in token_predictions if pred != -100), -100)  # First valid prediction\n",
        "\n",
        "    # Append predictions to the respective lists\n",
        "    sentence_predictions_majority.append(sentence_pred_majority)\n",
        "    sentence_predictions_first_non_negative.append(sentence_pred_first_non_negative)\n",
        "\n",
        "# Map the sentence-level predictions back to the DataFrame\n",
        "for i, sentence in enumerate(prediction_df['sentence'].unique()):\n",
        "    prediction_df.loc[prediction_df['sentence'] == sentence, 'tp_h_sentence_pred_majority'] = sentence_predictions_majority[i]\n",
        "    prediction_df.loc[prediction_df['sentence'] == sentence, 'tp_h_sentence_pred_first_non_negative'] = sentence_predictions_first_non_negative[i]\n",
        "\n",
        "# Calculate prevalence for both methods\n",
        "majority_positive_count = (prediction_df['tp_h_sentence_pred_majority'] > 0).sum()\n",
        "first_non_negative_positive_count = (prediction_df['tp_h_sentence_pred_first_non_negative'] > 0).sum()\n",
        "total_sentences = prediction_df['sentence'].nunique()\n",
        "\n",
        "majority_prevalence = majority_positive_count / total_sentences * 100  # Prevalence in percentage\n",
        "first_non_negative_prevalence = first_non_negative_positive_count / total_sentences * 100  # Prevalence in percentage\n",
        "\n",
        "# Display prevalence results\n",
        "print(f\"Majority Voting Prevalence: {majority_prevalence:.2f}%\")\n",
        "print(f\"First Non-Negative Rule Prevalence: {first_non_negative_prevalence:.2f}%\")\n",
        "\n",
        "# Display the updated DataFrame\n",
        "print(prediction_df[['sentence', 'tp_h_token_pred', 'tp_h_sentence_pred_majority', 'tp_h_sentence_pred_first_non_negative']])\n",
        "\n"
      ],
      "metadata": {
        "id": "ats9EwBtHdcn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "55aa1a48-d61c-42ed-da76-21a3d51a7153"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Majority Voting Prevalence: 6.78%\n",
            "First Non-Negative Rule Prevalence: 3.29%\n",
            "                                                sentence  \\\n",
            "0      Caro Antonio, Cari amici, Mr. Minister, Mr. Ma...   \n",
            "1      I am hesitating between English and French, bu...   \n",
            "2      And also because the French will have election...   \n",
            "3      But before I do this, I have to tell you that ...   \n",
            "4      And so I can't make a masterful speech, but I'...   \n",
            "...                                                  ...   \n",
            "74884  Ahead of the European elections, we need to sh...   \n",
            "74885  The European Border and Coast Guard is a case ...   \n",
            "74886  A better protection of our external borders is...   \n",
            "74887  Today we show how the EU matters and how it ca...   \n",
            "74888                                         Thank you.   \n",
            "\n",
            "                                         tp_h_token_pred  \\\n",
            "0      [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "1      [0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, ...   \n",
            "2      [0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, ...   \n",
            "3      [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "4      [0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "...                                                  ...   \n",
            "74884  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "74885  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "74886  [0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, ...   \n",
            "74887  [0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, ...   \n",
            "74888  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "\n",
            "      tp_h_sentence_pred_majority tp_h_sentence_pred_first_non_negative  \n",
            "0                               0                                     0  \n",
            "1                               1                                     0  \n",
            "2                               0                                     0  \n",
            "3                               0                                     0  \n",
            "4                               0                                     0  \n",
            "...                           ...                                   ...  \n",
            "74884                           0                                     0  \n",
            "74885                           0                                     0  \n",
            "74886                           0                                     0  \n",
            "74887                           0                                     0  \n",
            "74888                           0                                     0  \n",
            "\n",
            "[74889 rows x 4 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the updated DataFrame with sentence-level predictions\n",
        "output_path = '/content/gdrive/MyDrive/TimePressure/migration_sample_with_sentence_predictions_h.feather'\n",
        "prediction_df.to_feather(output_path)\n",
        "\n",
        "print(f\"DataFrame with predictions saved to: {output_path}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FtLUaVxauDzt",
        "outputId": "5717de8d-71cc-4e7b-e3fe-045dd9b563ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DataFrame with predictions saved to: /content/gdrive/MyDrive/TimePressure/migration_sample_with_sentence_predictions_h.feather\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Load the saved DataFrame\n",
        "output_path = '/content/gdrive/MyDrive/TimePressure/migration_sample_with_sentence_predictions_h.feather'\n",
        "predicted_tp_h_df = pd.read_feather(output_path)\n",
        "\n",
        "# Display all column names\n",
        "print(\"Columns in the saved DataFrame:\")\n",
        "print(predicted_tp_h_df.columns.tolist())\n",
        "\n",
        "# Optionally, display the first few rows to see sample data\n",
        "print(\"\\nSample data from the saved DataFrame:\")\n",
        "print(predicted_tp_h_df.head())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IZ0C6eguP0PK",
        "outputId": "8b42ac67-91e6-4020-d179-4d6245d24e6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Columns in the saved DataFrame:\n",
            "['speech_id', 'sentence_id', 'date', 'title', 'speaker', 'portfolio', 'context', 'pdfurl', 'url', 'text.annotated', 'after.sampling', 'length', 'langdetect', 'sentence_text', 'title_translated', 'pdfonly', 'probs', 'langdetect_sentence', 'sentence_translated', 'sentence_text_en', 'year', 'sentence', 'tp_h_token_pred', 'tp_h_sentence_pred_majority', 'tp_h_sentence_pred_first_non_negative']\n",
            "\n",
            "Sample data from the saved DataFrame:\n",
            "   speech_id  sentence_id       date  \\\n",
            "0          0            1 2017-05-05   \n",
            "1          0            2 2017-05-05   \n",
            "2          0            3 2017-05-05   \n",
            "3          0            4 2017-05-05   \n",
            "4          0            5 2017-05-05   \n",
            "\n",
            "                                               title              speaker  \\\n",
            "0  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "1  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "2  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "3  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "4  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "\n",
            "   portfolio context                                             pdfurl  \\\n",
            "0  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "1  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "2  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "3  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "4  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "\n",
            "                                                 url text.annotated  ...  \\\n",
            "0  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "1  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "2  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "3  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "4  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "\n",
            "   pdfonly     probs langdetect_sentence  \\\n",
            "0    FALSE  0.785897                  en   \n",
            "1    FALSE  0.970456                  en   \n",
            "2    FALSE  0.991257                  en   \n",
            "3    FALSE  0.995607                  fr   \n",
            "4    FALSE   0.99236                  fr   \n",
            "\n",
            "                                 sentence_translated  \\\n",
            "0  Caro Antonio, Cari amici, Mr. Minister, Mr. Ma...   \n",
            "1  I am hesitating between English and French, bu...   \n",
            "2  And also because the French will have election...   \n",
            "3  But before I do this, I have to tell you that ...   \n",
            "4  And so I can't make a masterful speech, but I'...   \n",
            "\n",
            "                                    sentence_text_en  year  \\\n",
            "0  Caro Antonio, Cari amici, Mr. Minister, Mr. Ma...  2017   \n",
            "1  I am hesitating between English and French, bu...  2017   \n",
            "2  And also because the French will have election...  2017   \n",
            "3  But before I do this, I have to tell you that ...  2017   \n",
            "4  And so I can't make a masterful speech, but I'...  2017   \n",
            "\n",
            "                                            sentence  \\\n",
            "0  Caro Antonio, Cari amici, Mr. Minister, Mr. Ma...   \n",
            "1  I am hesitating between English and French, bu...   \n",
            "2  And also because the French will have election...   \n",
            "3  But before I do this, I have to tell you that ...   \n",
            "4  And so I can't make a masterful speech, but I'...   \n",
            "\n",
            "                                     tp_h_token_pred  \\\n",
            "0  [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "1  [0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, ...   \n",
            "2  [0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, ...   \n",
            "3  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "4  [0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "\n",
            "  tp_h_sentence_pred_majority tp_h_sentence_pred_first_non_negative  \n",
            "0                           0                                     0  \n",
            "1                           1                                     0  \n",
            "2                           0                                     0  \n",
            "3                           0                                     0  \n",
            "4                           0                                     0  \n",
            "\n",
            "[5 rows x 25 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 8.2.3 Visualize predictions"
      ],
      "metadata": {
        "id": "Se71e6kR2iLu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Contrast predictions with annotations"
      ],
      "metadata": {
        "id": "L-5mD42clgi9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "# Assuming prediction_df is already loaded and contains 'date', 'tp_h_sentence_pred', and 'portfolio' columns\n",
        "\n",
        "# Ensure the date column is in datetime format\n",
        "predicted_tp_h_df['date'] = pd.to_datetime(predicted_tp_h_df['date'])\n",
        "\n",
        "# Extract the year from the date column\n",
        "predicted_tp_h_df['year'] = predicted_tp_h_df['date'].dt.year\n",
        "\n",
        "# Calculate positive sentences per year for 'President' portfolio\n",
        "president_df = predicted_tp_h_df[predicted_tp_h_df['portfolio'] == 'President']\n",
        "positive_sentences_president = president_df.groupby('year')['tp_h_sentence_pred_first_non_negative'].sum()\n",
        "unique_speech_ids_president = president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Calculate positive sentences per year for non-'President' portfolios\n",
        "non_president_df = predicted_tp_h_df[predicted_tp_h_df['portfolio'] != 'President']\n",
        "positive_sentences_non_president = non_president_df.groupby('year')['tp_h_sentence_pred_first_non_negative'].sum()\n",
        "unique_speech_ids_non_president = non_president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Calculate combined positive sentences per year\n",
        "combined_sentences_per_year = positive_sentences_president + positive_sentences_non_president\n",
        "\n",
        "# Plot the results\n",
        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "\n",
        "# Plot combined line\n",
        "ax1.plot(combined_sentences_per_year.index, combined_sentences_per_year.values, marker='o', color='black', linestyle='-', label='Implicit time pressure invocations')\n",
        "# Set y-axis limits for ax1\n",
        "ax1.set_ylim(0, 400)\n",
        "\n",
        "# Bar plot for unique speech_id for both 'President' and non-'President' portfolios\n",
        "ax2 = ax1.twinx()\n",
        "ax2.bar(unique_speech_ids_president.index, unique_speech_ids_president.values, width=0.4, align='center', color='lightblue', alpha=0.5, label='Speeches President')\n",
        "ax2.bar(unique_speech_ids_non_president.index + 0.4, unique_speech_ids_non_president.values, width=0.4, align='center', color='lightgrey', alpha=0.5, label='Speeches Commissioner')\n",
        "ax2.set_ylabel('Number of speeches', color='gray')\n",
        "ax2.tick_params(axis='y', labelcolor='gray')\n",
        "\n",
        "# Set x-axis to show every year and rotate labels vertically\n",
        "ax1.set_xticks(range(predicted_tp_h_df['year'].min(), predicted_tp_h_df['year'].max() + 1))\n",
        "ax1.set_xticklabels(range(predicted_tp_h_df['year'].min(), predicted_tp_h_df['year'].max() + 1), rotation=90)\n",
        "\n",
        "# Add vertical lines with labels for Commissioner tenures\n",
        "events = {\n",
        "    1995: 'Gradin',\n",
        "    1999: 'Vitorino',\n",
        "    2004: 'Fratini',\n",
        "    2008: 'Barrot',\n",
        "    2010: 'Malmström',\n",
        "    2014: 'Avramopoulos',\n",
        "    2019: 'Johansson'\n",
        "}\n",
        "\n",
        "for year, name in events.items():\n",
        "    ax1.axvline(x=year, color='darkgrey', linestyle='--', alpha=0.9)\n",
        "    ax1.text(year, ax1.get_ylim()[0] + 200, name, color='darkgrey', verticalalignment='center', horizontalalignment='right', rotation=90)\n",
        "\n",
        "# Add legend\n",
        "lines, labels = ax1.get_legend_handles_labels()\n",
        "lines2, labels2 = ax2.get_legend_handles_labels()\n",
        "ax1.legend(lines + lines2, labels + labels2, loc='upper left')\n",
        "\n",
        "# Adjust layout\n",
        "fig.tight_layout()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "FPRXfiAAe1Qy",
        "outputId": "1aea9ef8-78b2-4213-c63a-4fe3ab2b288b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Add asylum numbers to graph"
      ],
      "metadata": {
        "id": "7-toum4GbfZl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "# Load the asylum data and prediction data (asylum_nr_year should match with years from predicted_tp_h_df)\n",
        "asylum_path = '/content/gdrive/MyDrive/TimePressure/asylum_year.xlsx'\n",
        "asylum_df = pd.read_excel(asylum_path)\n",
        "\n",
        "# Rename 'asylum_nr_year' for consistency and load as time series data\n",
        "asylum_df.rename(columns={'asylum_nr_year': 'asylum_total'}, inplace=True)\n",
        "asylum_df['year'] = asylum_df['year']\n",
        "\n",
        "# Scale down asylum numbers for visibility\n",
        "asylum_df['asylum_total_scaled'] = asylum_df['asylum_total'] / 10000\n",
        "\n",
        "# Assuming prediction_df is already loaded and contains 'date', 'tp_h_sentence_pred', and 'portfolio' columns\n",
        "predicted_tp_h_df['date'] = pd.to_datetime(predicted_tp_h_df['date'])\n",
        "predicted_tp_h_df['year'] = predicted_tp_h_df['date'].dt.year\n",
        "\n",
        "# Calculate positive sentences per year for 'President' portfolio\n",
        "president_df = predicted_tp_h_df[predicted_tp_h_df['portfolio'] == 'President']\n",
        "positive_sentences_president = president_df.groupby('year')['tp_h_sentence_pred_first_non_negative'].sum()\n",
        "unique_speech_ids_president = president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Calculate positive sentences per year for non-'President' portfolios\n",
        "non_president_df = predicted_tp_h_df[predicted_tp_h_df['portfolio'] != 'President']\n",
        "positive_sentences_non_president = non_president_df.groupby('year')['tp_h_sentence_pred_first_non_negative'].sum()\n",
        "unique_speech_ids_non_president = non_president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Calculate combined positive sentences per year\n",
        "combined_sentences_per_year = positive_sentences_president + positive_sentences_non_president\n",
        "\n",
        "# Plot the results\n",
        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "# Plot combined line\n",
        "ax1.plot(combined_sentences_per_year.index, combined_sentences_per_year.values, marker='o', color='black', linestyle='-', label='Implicit Time Pressure Invocations')\n",
        "ax1.set_ylim(0, 400)\n",
        "\n",
        "# Add scaled asylum data as a secondary line on ax1\n",
        "ax1.plot(asylum_df['year'], asylum_df['asylum_total_scaled'], color='darkgrey', marker='s', linestyle='-', label='Yearly European Asylum Numbers (10,000)')\n",
        "ax1.set_ylabel('Implicit Invocations of Time Pressure', color='black')\n",
        "\n",
        "# Bar plot for unique speech_id for both 'President' and non-'President' portfolios\n",
        "ax2 = ax1.twinx()\n",
        "ax2.bar(unique_speech_ids_president.index, unique_speech_ids_president.values, width=0.4, align='center', color='lightblue', alpha=0.5, label='Speeches President')\n",
        "ax2.bar(unique_speech_ids_non_president.index + 0.4, unique_speech_ids_non_president.values, width=0.4, align='center', color='lightgrey', alpha=0.5, label='Speeches Commissioner')\n",
        "ax2.set_ylabel('Number of speeches', color='gray')\n",
        "ax2.tick_params(axis='y', labelcolor='gray')\n",
        "\n",
        "# Set x-axis to show every year and rotate labels vertically\n",
        "ax1.set_xticks(range(predicted_tp_h_df['year'].min(), predicted_tp_h_df['year'].max() + 1))\n",
        "ax1.set_xticklabels(range(predicted_tp_h_df['year'].min(), predicted_tp_h_df['year'].max() + 1), rotation=90)\n",
        "\n",
        "# Add vertical lines with labels for Commissioner tenures\n",
        "events = {\n",
        "    1995: 'Gradin',\n",
        "    1999: 'Vitorino',\n",
        "    2004: 'Fratini',\n",
        "    2008: 'Barrot',\n",
        "    2010: 'Malmström',\n",
        "    2014: 'Avramopoulos',\n",
        "    2019: 'Johansson'\n",
        "}\n",
        "\n",
        "for year, name in events.items():\n",
        "    ax1.axvline(x=year, color='darkgrey', linestyle='--', alpha=0.9)\n",
        "    ax1.text(year, ax1.get_ylim()[0] + 200, name, color='darkgrey', verticalalignment='center', horizontalalignment='right', rotation=90)\n",
        "\n",
        "# Add legend\n",
        "lines, labels = ax1.get_legend_handles_labels()\n",
        "lines2, labels2 = ax2.get_legend_handles_labels()\n",
        "ax1.legend(lines + lines2, labels + labels2, loc='upper left')\n",
        "\n",
        "# Adjust layout\n",
        "fig.tight_layout()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "74KnWBcUmDEe",
        "outputId": "c55abc55-d2e9-442c-9c8c-28bbfa20a92c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TM4Q0e_Zif3X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "# Assuming prediction_df is already loaded and contains 'date', 'tp_h_sentence_pred', and 'portfolio' columns\n",
        "\n",
        "# Ensure the date column is in datetime format\n",
        "prediction_df['date'] = pd.to_datetime(prediction_df['date'])\n",
        "\n",
        "# Extract the year from the date column\n",
        "prediction_df['year'] = prediction_df['date'].dt.year\n",
        "\n",
        "# Calculate positive sentences per year for 'President' portfolio\n",
        "president_df = prediction_df[prediction_df['portfolio'] == 'President']\n",
        "positive_sentences_president = president_df.groupby('year')['tp_h_sentence_pred_majority'].sum()\n",
        "unique_speech_ids_president = president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Calculate positive sentences per year for non-'President' portfolios\n",
        "non_president_df = prediction_df[prediction_df['portfolio'] != 'President']\n",
        "positive_sentences_non_president = non_president_df.groupby('year')['tp_h_sentence_pred_majority'].sum()\n",
        "unique_speech_ids_non_president = non_president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Calculate combined positive sentences per year\n",
        "combined_sentences_per_year = positive_sentences_president + positive_sentences_non_president\n",
        "\n",
        "# Plot the results\n",
        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "\n",
        "# Plot combined line\n",
        "ax1.plot(combined_sentences_per_year.index, combined_sentences_per_year.values, marker='o', color='black', linestyle='-', label='Implicit time pressure invocations')\n",
        "ax1.set_ylabel('Number of Implicit Time Pressure Invocations', color='black')\n",
        "ax1.set_ylim(0, 1000)\n",
        "\n",
        "# Bar plot for unique speech_id for both 'President' and non-'President' portfolios\n",
        "ax2 = ax1.twinx()\n",
        "ax2.bar(unique_speech_ids_president.index, unique_speech_ids_president.values, width=0.4, align='center', color='lightblue', alpha=0.5, label='Speeches President')\n",
        "ax2.bar(unique_speech_ids_non_president.index + 0.4, unique_speech_ids_non_president.values, width=0.4, align='center', color='lightgrey', alpha=0.5, label='Speeches Commissioner')\n",
        "ax2.set_ylabel('Number of speeches', color='gray')\n",
        "ax2.tick_params(axis='y', labelcolor='gray')\n",
        "\n",
        "# Set x-axis to show every year and rotate labels vertically\n",
        "ax1.set_xticks(range(prediction_df['year'].min(), prediction_df['year'].max() + 1))\n",
        "ax1.set_xticklabels(range(prediction_df['year'].min(), prediction_df['year'].max() + 1), rotation=90)\n",
        "\n",
        "# Add vertical lines with labels for Commissioner tenures\n",
        "events = {\n",
        "    1995: 'Gradin',\n",
        "    1999: 'Vitorino',\n",
        "    2004: 'Fratini',\n",
        "    2008: 'Barrot',\n",
        "    2010: 'Malmström',\n",
        "    2014: 'Avramopoulos',\n",
        "    2019: 'Johansson'\n",
        "}\n",
        "\n",
        "for year, name in events.items():\n",
        "    ax1.axvline(x=year, color='darkgrey', linestyle='--', alpha=0.9)\n",
        "    ax1.text(year, ax1.get_ylim()[0] + 750, name, color='darkgrey', verticalalignment='center', horizontalalignment='right', rotation=90)\n",
        "\n",
        "# Add legend\n",
        "lines, labels = ax1.get_legend_handles_labels()\n",
        "lines2, labels2 = ax2.get_legend_handles_labels()\n",
        "ax1.legend(lines + lines2, labels + labels2, loc='upper left')\n",
        "\n",
        "# Adjust layout\n",
        "fig.tight_layout()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "Ls3G-uz4zgxl",
        "outputId": "bca212e9-16a7-4f39-b5f4-b017660ffd01"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'prediction_df' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-0735e34952d4>\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# Ensure the date column is in datetime format\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mprediction_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'date'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprediction_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'date'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# Extract the year from the date column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'prediction_df' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "# Assuming prediction_df is already loaded and contains 'date', 'tp_h_sentence_pred', and 'portfolio' columns\n",
        "\n",
        "# Ensure the date column is in datetime format\n",
        "prediction_df['date'] = pd.to_datetime(prediction_df['date'])\n",
        "\n",
        "# Extract the year from the date column\n",
        "prediction_df['year'] = prediction_df['date'].dt.year\n",
        "\n",
        "# Calculate positive sentences per year for 'President' portfolio\n",
        "president_df = prediction_df[prediction_df['portfolio'] == 'President']\n",
        "positive_sentences_president = president_df.groupby('year')['tp_h_sentence_pred_first_non_negative'].sum()\n",
        "unique_speech_ids_president = president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Calculate positive sentences per year for non-'President' portfolios\n",
        "non_president_df = prediction_df[prediction_df['portfolio'] != 'President']\n",
        "positive_sentences_non_president = non_president_df.groupby('year')['tp_h_sentence_pred_first_non_negative'].sum()\n",
        "unique_speech_ids_non_president = non_president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Calculate combined positive sentences per year\n",
        "combined_sentences_per_year = positive_sentences_president + positive_sentences_non_president\n",
        "\n",
        "# Plot the results\n",
        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "\n",
        "# Plot combined line\n",
        "ax1.plot(combined_sentences_per_year.index, combined_sentences_per_year.values, marker='o', color='black', linestyle='-', label='Implicit time pressure invocations')\n",
        "ax1.set_ylabel('Number of Implicit Time Pressure Invocations', color='black')\n",
        "ax1.set_ylim(0, 1000)\n",
        "\n",
        "# Bar plot for unique speech_id for both 'President' and non-'President' portfolios\n",
        "ax2 = ax1.twinx()\n",
        "ax2.bar(unique_speech_ids_president.index, unique_speech_ids_president.values, width=0.4, align='center', color='lightblue', alpha=0.5, label='Speeches President')\n",
        "ax2.bar(unique_speech_ids_non_president.index + 0.4, unique_speech_ids_non_president.values, width=0.4, align='center', color='lightgrey', alpha=0.5, label='Speeches Commissioner')\n",
        "ax2.set_ylabel('Number of speeches', color='gray')\n",
        "ax2.tick_params(axis='y', labelcolor='gray')\n",
        "\n",
        "# Set x-axis to show every year and rotate labels vertically\n",
        "ax1.set_xticks(range(prediction_df['year'].min(), prediction_df['year'].max() + 1))\n",
        "ax1.set_xticklabels(range(prediction_df['year'].min(), prediction_df['year'].max() + 1), rotation=90)\n",
        "\n",
        "# Add vertical lines with labels for Commissioner tenures\n",
        "events = {\n",
        "    1995: 'Gradin',\n",
        "    1999: 'Vitorino',\n",
        "    2004: 'Fratini',\n",
        "    2008: 'Barrot',\n",
        "    2010: 'Malmström',\n",
        "    2014: 'Avramopoulos',\n",
        "    2019: 'Johansson'\n",
        "}\n",
        "\n",
        "for year, name in events.items():\n",
        "    ax1.axvline(x=year, color='darkgrey', linestyle='--', alpha=0.9)\n",
        "    ax1.text(year, ax1.get_ylim()[0] + 750, name, color='darkgrey', verticalalignment='center', horizontalalignment='right', rotation=90)\n",
        "\n",
        "# Add legend\n",
        "lines, labels = ax1.get_legend_handles_labels()\n",
        "lines2, labels2 = ax2.get_legend_handles_labels()\n",
        "ax1.legend(lines + lines2, labels + labels2, loc='upper left')\n",
        "\n",
        "# Adjust layout\n",
        "fig.tight_layout()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "ZRYalzU9SYqP",
        "outputId": "60a4ca2f-5613-4de5-e6f2-efa02db404d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "# Assuming prediction_df is already loaded and contains 'date', 'tp_h_sentence_pred_majority', and 'portfolio' columns\n",
        "\n",
        "# Ensure the date column is in datetime format\n",
        "prediction_df['date'] = pd.to_datetime(prediction_df['date'])\n",
        "\n",
        "# Extract the year from the date column\n",
        "prediction_df['year'] = prediction_df['date'].dt.year\n",
        "\n",
        "# Calculate positive sentences per year for 'President' portfolio\n",
        "president_df = prediction_df[prediction_df['portfolio'] == 'President']\n",
        "positive_sentences_president = president_df.groupby('year')['tp_h_sentence_pred_majority'].sum()\n",
        "unique_speech_ids_president = president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Calculate positive sentences per year for non-'President' portfolios\n",
        "non_president_df = prediction_df[prediction_df['portfolio'] != 'President']\n",
        "positive_sentences_non_president = non_president_df.groupby('year')['tp_h_sentence_pred_majority'].sum()\n",
        "unique_speech_ids_non_president = non_president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Calculate combined positive sentences per year\n",
        "combined_sentences_per_year = positive_sentences_president + positive_sentences_non_president\n",
        "\n",
        "# Calculate average per speech\n",
        "average_sentences_president = positive_sentences_president / unique_speech_ids_president\n",
        "average_sentences_non_president = positive_sentences_non_president / unique_speech_ids_non_president\n",
        "average_combined = combined_sentences_per_year / (unique_speech_ids_president + unique_speech_ids_non_president)\n",
        "\n",
        "# Plot the results\n",
        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "# Plot combined line\n",
        "ax1.plot(average_combined.index, average_combined.values, marker='o', color='black', linestyle='-', label='Average Implicit Time Pressure Invocations per Speech')\n",
        "ax1.set_ylabel('Average Number of Implicit Time Pressure Invocations per Speech', color='black')\n",
        "ax1.set_ylim(0, 30)  # Adjust y-axis limit as needed\n",
        "\n",
        "# Bar plot for unique speech_id for both 'President' and non-'President' portfolios\n",
        "ax2 = ax1.twinx()\n",
        "ax2.bar(unique_speech_ids_president.index, unique_speech_ids_president.values, width=0.4, align='center', color='lightblue', alpha=0.5, label='Speeches President')\n",
        "ax2.bar(unique_speech_ids_non_president.index + 0.4, unique_speech_ids_non_president.values, width=0.4, align='center', color='lightgrey', alpha=0.5, label='Speeches Commissioner')\n",
        "ax2.set_ylabel('Number of Speeches', color='gray')\n",
        "ax2.tick_params(axis='y', labelcolor='gray')\n",
        "\n",
        "# Set x-axis to show every year and rotate labels vertically\n",
        "ax1.set_xticks(range(prediction_df['year'].min(), prediction_df['year'].max() + 1))\n",
        "ax1.set_xticklabels(range(prediction_df['year'].min(), prediction_df['year'].max() + 1), rotation=90)\n",
        "\n",
        "# Add vertical lines with labels for Commissioner tenures\n",
        "events = {\n",
        "    1995: 'Gradin',\n",
        "    1999: 'Vitorino',\n",
        "    2004: 'Fratini',\n",
        "    2008: 'Barrot',\n",
        "    2010: 'Malmström',\n",
        "    2014: 'Avramopoulos',\n",
        "    2019: 'Johansson'\n",
        "}\n",
        "\n",
        "for year, name in events.items():\n",
        "    ax1.axvline(x=year, color='darkgrey', linestyle='--', alpha=0.9)\n",
        "    ax1.text(year, ax1.get_ylim()[0] + 5, name, color='darkgrey', verticalalignment='center', horizontalalignment='right', rotation=90)\n",
        "\n",
        "# Add legend\n",
        "lines, labels = ax1.get_legend_handles_labels()\n",
        "lines2, labels2 = ax2.get_legend_handles_labels()\n",
        "ax1.legend(lines + lines2, labels + labels2, loc='upper left')\n",
        "\n",
        "# Adjust layout\n",
        "fig.tight_layout()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "pdfNI55WgJCw",
        "outputId": "f87482b9-fb6a-484c-ad9e-cb38b59f96e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "# Assuming prediction_df is already loaded and contains 'date', 'tp_h_sentence_pred_majority', and 'portfolio' columns\n",
        "\n",
        "# Ensure the date column is in datetime format\n",
        "prediction_df['date'] = pd.to_datetime(prediction_df['date'])\n",
        "\n",
        "# Extract the year from the date column\n",
        "prediction_df['year'] = prediction_df['date'].dt.year\n",
        "\n",
        "# Calculate total sentences per year for 'President' portfolio\n",
        "president_df = prediction_df[prediction_df['portfolio'] == 'President']\n",
        "total_sentences_president = president_df.groupby('year')['sentence'].count()  # Count total sentences\n",
        "positive_sentences_president = president_df.groupby('year')['tp_h_sentence_pred_majority'].sum()\n",
        "\n",
        "# Calculate total sentences per year for non-'President' portfolios\n",
        "non_president_df = prediction_df[prediction_df['portfolio'] != 'President']\n",
        "total_sentences_non_president = non_president_df.groupby('year')['sentence'].count()  # Count total sentences\n",
        "positive_sentences_non_president = non_president_df.groupby('year')['tp_h_sentence_pred_majority'].sum()\n",
        "\n",
        "# Calculate combined total and positive sentences per year\n",
        "total_sentences_combined = total_sentences_president + total_sentences_non_president\n",
        "combined_positive_sentences = positive_sentences_president + positive_sentences_non_president\n",
        "\n",
        "# Calculate percentage of positive invocations relative to total sentences\n",
        "percentage_positive_president = (positive_sentences_president / total_sentences_president) * 100\n",
        "percentage_positive_non_president = (positive_sentences_non_president / total_sentences_non_president) * 100\n",
        "percentage_combined = (combined_positive_sentences / total_sentences_combined) * 100\n",
        "\n",
        "# Plot the results\n",
        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "# Plot combined percentage line\n",
        "ax1.plot(percentage_combined.index, percentage_combined.values, marker='o', color='black', linestyle='-', label='Percentage of Implicit Time Pressure Invocations per Speech')\n",
        "ax1.set_ylabel('Percentage of Implicit Time Pressure Invocations', color='black')\n",
        "ax1.set_ylim(0, 100)  # Set y-axis limit as needed\n",
        "\n",
        "# Bar plot for total sentences for both 'President' and non-'President' portfolios\n",
        "ax2 = ax1.twinx()\n",
        "ax2.bar(total_sentences_president.index, total_sentences_president.values, width=0.4, align='center', color='lightblue', alpha=0.5, label='Total Sentences President')\n",
        "ax2.bar(total_sentences_non_president.index + 0.4, total_sentences_non_president.values, width=0.4, align='center', color='lightgrey', alpha=0.5, label='Total Sentences Commissioner')\n",
        "ax2.set_ylabel('Total Number of Sentences', color='gray')\n",
        "ax2.tick_params(axis='y', labelcolor='gray')\n",
        "\n",
        "# Set x-axis to show every year and rotate labels vertically\n",
        "ax1.set_xticks(range(prediction_df['year'].min(), prediction_df['year'].max() + 1))\n",
        "ax1.set_xticklabels(range(prediction_df['year'].min(), prediction_df['year'].max() + 1), rotation=90)\n",
        "\n",
        "# Add vertical lines with labels for Commissioner tenures\n",
        "events = {\n",
        "    1995: 'Gradin',\n",
        "    1999: 'Vitorino',\n",
        "    2004: 'Fratini',\n",
        "    2008: 'Barrot',\n",
        "    2010: 'Malmström',\n",
        "    2014: 'Avramopoulos',\n",
        "    2019: 'Johansson'\n",
        "}\n",
        "\n",
        "for year, name in events.items():\n",
        "    ax1.axvline(x=year, color='darkgrey', linestyle='--', alpha=0.9)\n",
        "    ax1.text(year, ax1.get_ylim()[0] + 5, name, color='darkgrey', verticalalignment='center', horizontalalignment='right', rotation=90)\n",
        "\n",
        "# Add legend\n",
        "lines, labels = ax1.get_legend_handles_labels()\n",
        "lines2, labels2 = ax2.get_legend_handles_labels()\n",
        "ax1.legend(lines + lines2, labels + labels2, loc='upper left')\n",
        "\n",
        "# Adjust layout\n",
        "fig.tight_layout()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "HSvIocopgxmC",
        "outputId": "1f6e8324-4b9a-46f6-8087-fa8763f695c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "# Assuming prediction_df is already loaded and contains 'date', 'tp_h_sentence_pred_majority', and 'portfolio' columns\n",
        "\n",
        "# Ensure the date column is in datetime format\n",
        "prediction_df['date'] = pd.to_datetime(prediction_df['date'])\n",
        "\n",
        "# Extract the year from the date column\n",
        "prediction_df['year'] = prediction_df['date'].dt.year\n",
        "\n",
        "# Calculate total sentences per year for 'President' portfolio\n",
        "president_df = prediction_df[prediction_df['portfolio'] == 'President']\n",
        "total_sentences_president = president_df.groupby('year')['sentence'].count()  # Count total sentences\n",
        "positive_sentences_president = president_df.groupby('year')['tp_h_sentence_pred_majority'].sum()\n",
        "\n",
        "# Calculate total sentences per year for non-'President' portfolios\n",
        "non_president_df = prediction_df[prediction_df['portfolio'] != 'President']\n",
        "total_sentences_non_president = non_president_df.groupby('year')['sentence'].count()  # Count total sentences\n",
        "positive_sentences_non_president = non_president_df.groupby('year')['tp_h_sentence_pred_majority'].sum()\n",
        "\n",
        "# Calculate combined total and positive sentences per year\n",
        "total_sentences_combined = total_sentences_president + total_sentences_non_president\n",
        "combined_positive_sentences = positive_sentences_president + positive_sentences_non_president\n",
        "\n",
        "# Calculate percentage of positive invocations relative to total sentences\n",
        "percentage_positive_president = (positive_sentences_president / total_sentences_president) * 100\n",
        "percentage_positive_non_president = (positive_sentences_non_president / total_sentences_non_president) * 100\n",
        "percentage_combined = (combined_positive_sentences / total_sentences_combined) * 100\n",
        "\n",
        "# Plot the results\n",
        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "# Plot percentage for President portfolio\n",
        "ax1.plot(percentage_positive_president.index, percentage_positive_president.values, marker='o', color='blue', linestyle='-', label='President (%)')\n",
        "# Plot percentage for Commissioner portfolio\n",
        "ax1.plot(percentage_positive_non_president.index, percentage_positive_non_president.values, marker='o', color='orange', linestyle='-', label='Commissioner (%)')\n",
        "# Plot combined percentage line\n",
        "ax1.plot(percentage_combined.index, percentage_combined.values, marker='o', color='black', linestyle='-', label='Combined (%)')\n",
        "\n",
        "# Set y-axis labels and limits\n",
        "ax1.set_ylabel('Percentage of Implicit Time Pressure Invocations', color='black')\n",
        "ax1.set_ylim(0, 100)  # Set y-axis limit as needed\n",
        "\n",
        "# Bar plot for total sentences for both 'President' and non-'President' portfolios\n",
        "ax2 = ax1.twinx()\n",
        "ax2.bar(total_sentences_president.index, total_sentences_president.values, width=0.4, align='center', color='lightblue', alpha=0.5, label='Total Sentences President')\n",
        "ax2.bar(total_sentences_non_president.index + 0.4, total_sentences_non_president.values, width=0.4, align='center', color='lightgrey', alpha=0.5, label='Total Sentences Commissioner')\n",
        "ax2.set_ylabel('Total Number of Sentences', color='gray')\n",
        "ax2.tick_params(axis='y', labelcolor='gray')\n",
        "\n",
        "# Set x-axis to show every year and rotate labels vertically\n",
        "ax1.set_xticks(range(prediction_df['year'].min(), prediction_df['year'].max() + 1))\n",
        "ax1.set_xticklabels(range(prediction_df['year'].min(), prediction_df['year'].max() + 1), rotation=90)\n",
        "\n",
        "# Add vertical lines with labels for Commissioner tenures\n",
        "events = {\n",
        "    1995: 'Gradin',\n",
        "    1999: 'Vitorino',\n",
        "    2004: 'Fratini',\n",
        "    2008: 'Barrot',\n",
        "    2010: 'Malmström',\n",
        "    2014: 'Avramopoulos',\n",
        "    2019: 'Johansson'\n",
        "}\n",
        "\n",
        "for year, name in events.items():\n",
        "    ax1.axvline(x=year, color='darkgrey', linestyle='--', alpha=0.9)\n",
        "    ax1.text(year, ax1.get_ylim()[0] + 50, name, color='darkgrey', verticalalignment='center', horizontalalignment='right', rotation=90)\n",
        "\n",
        "# Add legend\n",
        "lines, labels = ax1.get_legend_handles_labels()\n",
        "lines2, labels2 = ax2.get_legend_handles_labels()\n",
        "ax1.legend(lines + lines2, labels + labels2, loc='upper left')\n",
        "\n",
        "# Adjust layout\n",
        "fig.tight_layout()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "CDgiC0ZdhYph",
        "outputId": "339eddb4-d427-4868-d117-1c933d99f40d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nxqimfHBThZt"
      },
      "source": [
        "# 9) Token level Transformer - Explicit time pressure"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 9.1 Load and pre-process data"
      ],
      "metadata": {
        "id": "9LYTYmWSIPLn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import re\n",
        "from collections import defaultdict\n",
        "import torch\n",
        "from transformers import BertTokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "# Load the Excel file into a DataFrame\n",
        "excel_file_path = '/content/gdrive/MyDrive/TimePressure/multilabel_gold_all_final.xlsx'\n",
        "result_df = pd.read_excel(excel_file_path)\n",
        "\n",
        "# Initialize lists to store token-level data\n",
        "token_list = []\n",
        "label_list = []\n",
        "\n",
        "# Define a function to split a sentence into tokens\n",
        "def tokenize(sentence):\n",
        "    # Split the sentence into tokens (you may need to customize this based on your tokenization requirements)\n",
        "    return sentence.split()\n",
        "\n",
        "# Remove annotation markers for non-classified labels and create token-level data\n",
        "for _, row in result_df.iterrows():\n",
        "    # Convert the Sentence column to a string (if not already)\n",
        "    sentence = str(row['Sentence'])\n",
        "\n",
        "    # Check if tp_h label is not equal to 1\n",
        "    if row['resp_f'] != 1:\n",
        "        # Remove square brackets and their contents from the Sentence column\n",
        "        sentence = re.sub(r'\\[[^\\]]*\\]', '', sentence)\n",
        "\n",
        "    # Split the cleaned sentence into tokens\n",
        "    tokens = tokenize(sentence)\n",
        "\n",
        "    # Initialize a dictionary to store token-level labels\n",
        "    token_labels = defaultdict(int)\n",
        "\n",
        "    # Find the start and end indices of [s] and [tp_h] markers\n",
        "    start_index = sentence.find('[s]')\n",
        "    end_index = sentence.find('[resp_f]')\n",
        "\n",
        "    # Check if both start and end markers are found\n",
        "    if start_index != -1 and end_index != -1:\n",
        "        # Extract the token span between start and end indices\n",
        "        token_span = tokenize(sentence[start_index + len('[s]'):end_index])\n",
        "\n",
        "        # Assign label 1 to tokens within the span, 0 otherwise\n",
        "        for token in token_span:\n",
        "            token_labels[token] = 1\n",
        "\n",
        "    # Append token-label pairs to the lists\n",
        "    for token in tokens:\n",
        "        token_list.append(token)\n",
        "        label_list.append(token_labels[token])\n",
        "\n",
        "# Create a new DataFrame to store token-level data\n",
        "token_df = pd.DataFrame({'Token': token_list, 'Label': label_list})\n",
        "\n",
        "# Save the token-level DataFrame to a new Excel file\n",
        "token_df.to_excel('token_level_data.xlsx', index=False)\n",
        "\n",
        "# Load the token-level DataFrame\n",
        "token_df = pd.read_excel('token_level_data.xlsx')\n",
        "\n",
        "# Tokenizer\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "\n",
        "# Initialize lists to store token-level data and labels\n",
        "tokenized_texts = []\n",
        "labels = []\n",
        "\n",
        "# Iterate through each row of the token-level DataFrame\n",
        "for _, row in token_df.iterrows():\n",
        "    # Tokenize the token using the BERT tokenizer\n",
        "    tokens = tokenizer.tokenize(row['Token'])\n",
        "\n",
        "    # Add special tokens [CLS] and [SEP]\n",
        "    tokens = ['[CLS]'] + tokens + ['[SEP]']\n",
        "\n",
        "    # Add token IDs to the list of token-level data\n",
        "    token_ids = tokenizer.convert_tokens_to_ids(tokens)\n",
        "    tokenized_texts.append(token_ids)\n",
        "\n",
        "    # Add labels to the list of labels\n",
        "    labels.append(row['Label'])\n",
        "\n",
        "# Pad sequences to the maximum length\n",
        "max_len = max(len(seq) for seq in tokenized_texts)\n",
        "input_ids = pad_sequences(tokenized_texts, maxlen=max_len, dtype=\"long\", value=0, truncating=\"post\", padding=\"post\")\n",
        "\n",
        "# Convert lists to PyTorch tensors\n",
        "input_ids = torch.tensor(input_ids)\n",
        "labels = torch.tensor(labels)\n",
        "\n",
        "# Print the first few tokenized texts and labels\n",
        "print(\"Tokenized Texts:\", input_ids[:5])\n",
        "print(\"Labels:\", labels[:5])\n",
        "\n",
        "# Save the preprocessed DataFrame to a new Excel file\n",
        "result_df.to_excel('preprocessed_data.xlsx', index=False)"
      ],
      "metadata": {
        "id": "XZqAyQDRIWsb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 612,
          "referenced_widgets": [
            "f57ae486d37742268ab82b276972c3da",
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            "80c217339ebe4b0688756aab751fb3d9"
          ]
        },
        "outputId": "8334bc36-a708-445c-fcf6-7d597d0538c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f57ae486d37742268ab82b276972c3da"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d400a57ef63b4b36ab1466344d263a98"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5d93a5570500457fbd4fb2899e856615"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3d71f90978ae41f5a9f1eea475759524"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tokenized Texts: tensor([[  101,  2720,  1010,   102,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0],\n",
            "        [  101,  3472,  1010,   102,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0],\n",
            "        [  101,  6456,  1010,   102,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0],\n",
            "        [  101, 11218,   102,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0],\n",
            "        [  101,  1998,   102,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0]])\n",
            "Labels: tensor([0, 0, 0, 0, 0])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 9.1.2 Run BERT model explicit high (resp_f)\n"
      ],
      "metadata": {
        "id": "zLyj_2_2Ij0x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from transformers import BertTokenizer, BertForTokenClassification, AdamW, get_linear_schedule_with_warmup\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score\n",
        "from tqdm import tqdm\n",
        "from sklearn.model_selection import train_test_split\n",
        "import numpy as np\n",
        "\n",
        "# Set seeds for replicability\n",
        "seed_value = 1234\n",
        "os.environ['PYTHONHASHSEED'] = str(seed_value)\n",
        "np.random.seed(seed_value)\n",
        "torch.manual_seed(seed_value)\n",
        "torch.cuda.manual_seed(seed_value)\n",
        "torch.cuda.manual_seed_all(seed_value)  # if you are using multi-GPU\n",
        "\n",
        "# Load the token-level DataFrame\n",
        "token_df = pd.read_excel('token_level_data.xlsx')\n",
        "\n",
        "# Tokenizer\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "\n",
        "# Define your token-level dataset\n",
        "class TokenDataset(Dataset):\n",
        "    def __init__(self, tokenizer, token_df, max_length):\n",
        "        self.tokenizer = tokenizer\n",
        "        self.token_df = token_df\n",
        "        self.max_length = max_length\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.token_df)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        tokens = self.tokenizer.tokenize(self.token_df.loc[idx, 'Token'])\n",
        "        tokens = ['[CLS]'] + tokens + ['[SEP]']\n",
        "\n",
        "        input_ids = self.tokenizer.convert_tokens_to_ids(tokens)\n",
        "        input_ids = input_ids[:self.max_length] + [0] * (self.max_length - len(input_ids))\n",
        "\n",
        "        attention_mask = [1] * len(tokens)\n",
        "        attention_mask = attention_mask[:self.max_length] + [0] * (self.max_length - len(attention_mask))\n",
        "\n",
        "        label = self.token_df.loc[idx, 'Label']\n",
        "        label = [label] + [-100] * (self.max_length - 1)  # Pad labels with -100 for ignored tokens\n",
        "\n",
        "        return {\n",
        "            'input_ids': torch.tensor(input_ids),\n",
        "            'attention_mask': torch.tensor(attention_mask),\n",
        "            'labels': torch.tensor(label)\n",
        "        }\n",
        "\n",
        "# Custom collate function to handle padding\n",
        "def custom_collate_fn(batch):\n",
        "    input_ids = torch.stack([item['input_ids'] for item in batch])\n",
        "    attention_masks = torch.stack([item['attention_mask'] for item in batch])\n",
        "    labels = torch.stack([item['labels'] for item in batch])\n",
        "\n",
        "    return input_ids, attention_masks, labels\n",
        "\n",
        "# Calculate class weights\n",
        "class_counts = token_df['Label'].value_counts().to_dict()\n",
        "total_samples = len(token_df)\n",
        "class_weights = {cls: total_samples/count for cls, count in class_counts.items()}\n",
        "\n",
        "weights = [class_weights[cls] for cls in sorted(class_weights.keys())]\n",
        "weights = torch.tensor(weights, dtype=torch.float)\n",
        "\n",
        "# Define your training function\n",
        "def train_model(model, train_loader, val_loader, criterion, optimizer, scheduler, epochs):\n",
        "    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "    model.to(device)\n",
        "    criterion = criterion.to(device)\n",
        "\n",
        "    best_val_f1 = 0\n",
        "\n",
        "    for epoch in range(epochs):\n",
        "        model.train()\n",
        "        total_loss = 0.0\n",
        "\n",
        "        for batch in tqdm(train_loader, desc=f\"Training Epoch {epoch+1}/{epochs}\"):\n",
        "            input_ids, attention_masks, labels = batch\n",
        "            input_ids = input_ids.to(device)\n",
        "            attention_masks = attention_masks.to(device)\n",
        "            labels = labels.to(device)\n",
        "\n",
        "            optimizer.zero_grad()\n",
        "            outputs = model(input_ids=input_ids, attention_mask=attention_masks)\n",
        "            logits = outputs.logits\n",
        "\n",
        "            logits = logits.view(-1, model.config.num_labels)\n",
        "            labels = labels.view(-1)\n",
        "\n",
        "            loss = criterion(logits, labels)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            scheduler.step()\n",
        "\n",
        "            total_loss += loss.item()\n",
        "\n",
        "        avg_loss = total_loss / len(train_loader)\n",
        "        val_precision, val_recall, val_f1 = evaluate_model(model, val_loader, criterion)\n",
        "        print(f\"Epoch {epoch+1}/{epochs}, Loss: {avg_loss}, Validation Precision: {val_precision}, Validation Recall: {val_recall}, Validation F1 Score: {val_f1}\")\n",
        "\n",
        "        # Save model if it has improved\n",
        "        if val_f1 > best_val_f1:\n",
        "            best_val_f1 = val_f1\n",
        "            torch.save(model.state_dict(), 'best_model.pt')\n",
        "\n",
        "def evaluate_model(model, val_loader, criterion):\n",
        "    model.eval()\n",
        "    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "    all_preds = []\n",
        "    all_labels = []\n",
        "    total_loss = 0.0\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for batch in tqdm(val_loader, desc=\"Evaluating\"):\n",
        "            input_ids, attention_masks, labels = batch\n",
        "            input_ids = input_ids.to(device)\n",
        "            attention_masks = attention_masks.to(device)\n",
        "            labels = labels.to(device)\n",
        "\n",
        "            outputs = model(input_ids=input_ids, attention_mask=attention_masks)\n",
        "            logits = outputs.logits\n",
        "\n",
        "            logits = logits.view(-1, model.config.num_labels)\n",
        "            labels = labels.view(-1)\n",
        "\n",
        "            loss = criterion(logits, labels)\n",
        "            total_loss += loss.item()\n",
        "\n",
        "            preds = logits.argmax(dim=-1).cpu().numpy().flatten()\n",
        "            label_ids = labels.cpu().numpy().flatten()\n",
        "\n",
        "            mask = label_ids != -100  # Ignore padded labels\n",
        "            all_preds.extend(preds[mask].tolist())\n",
        "            all_labels.extend(label_ids[mask].tolist())\n",
        "\n",
        "    avg_loss = total_loss / len(val_loader)\n",
        "    precision = precision_score(all_labels, all_preds, average='weighted')\n",
        "    recall = recall_score(all_labels, all_preds, average='weighted')\n",
        "    f1 = f1_score(all_labels, all_preds, average='weighted')\n",
        "    return precision, recall, f1\n",
        "\n",
        "# Split the dataset\n",
        "train_df, temp_df = train_test_split(token_df, test_size=0.3, random_state=seed_value)\n",
        "val_df, test_df = train_test_split(temp_df, test_size=0.5, random_state=seed_value)\n",
        "\n",
        "# Reset indices after splitting\n",
        "train_df = train_df.reset_index(drop=True)\n",
        "val_df = val_df.reset_index(drop=True)\n",
        "test_df = test_df.reset_index(drop=True)\n",
        "\n",
        "# Token-level datasets\n",
        "max_length = 128\n",
        "train_dataset = TokenDataset(tokenizer, train_df, max_length=max_length)\n",
        "val_dataset = TokenDataset(tokenizer, val_df, max_length=max_length)\n",
        "test_dataset = TokenDataset(tokenizer, test_df, max_length=max_length)\n",
        "\n",
        "# DataLoaders for training, validation, and testing\n",
        "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, collate_fn=custom_collate_fn)\n",
        "val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False, collate_fn=custom_collate_fn)\n",
        "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False, collate_fn=custom_collate_fn)\n",
        "\n",
        "# BERT model\n",
        "model = BertForTokenClassification.from_pretrained('bert-base-uncased', num_labels=2)\n",
        "\n",
        "# Define criterion and optimizer\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "criterion = nn.CrossEntropyLoss(weight=weights)  # Use weights calculated earlier\n",
        "optimizer = AdamW(model.parameters(), lr=2e-5, eps=1e-8)\n",
        "scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=0, num_training_steps=len(train_loader)*10)  # Adjust num_training_steps as needed\n",
        "\n",
        "# Define number of epochs\n",
        "epochs = 4\n",
        "\n",
        "# Train the model\n",
        "train_model(model, train_loader, val_loader, criterion, optimizer, scheduler, epochs)\n",
        "\n",
        "# Evaluate the model on test data\n",
        "test_precision, test_recall, test_f1 = evaluate_model(model, test_loader, criterion)\n",
        "print(f\"Test Precision: {test_precision}, Test Recall: {test_recall}, Test F1 Score: {test_f1}\")"
      ],
      "metadata": {
        "id": "alHMX7krItpT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "59e2e62f-87fc-4b37-a57c-ef1a2cd8c288"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n",
            "Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:591: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n",
            "Training Epoch 1/4: 100%|██████████| 2200/2200 [05:49<00:00,  6.30it/s]\n",
            "Evaluating: 100%|██████████| 472/472 [00:26<00:00, 18.02it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/4, Loss: 0.6633340563489633, Validation Precision: 0.9448328490597798, Validation Recall: 0.9215569259332935, Validation F1 Score: 0.9326944804203277\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training Epoch 2/4: 100%|██████████| 2200/2200 [05:48<00:00,  6.32it/s]\n",
            "Evaluating: 100%|██████████| 472/472 [00:25<00:00, 18.48it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 2/4, Loss: 0.6269098117270253, Validation Precision: 0.944917842223846, Validation Recall: 0.9429082952058883, Validation F1 Score: 0.9439072738757297\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training Epoch 3/4: 100%|██████████| 2200/2200 [05:46<00:00,  6.35it/s]\n",
            "Evaluating: 100%|██████████| 472/472 [00:25<00:00, 18.47it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 3/4, Loss: 0.5972327834977345, Validation Precision: 0.9510366865613314, Validation Recall: 0.6558583648299184, Validation F1 Score: 0.7661710284424686\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training Epoch 4/4: 100%|██████████| 2200/2200 [05:46<00:00,  6.34it/s]\n",
            "Evaluating: 100%|██████████| 472/472 [00:25<00:00, 18.43it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 4/4, Loss: 0.5677683000402017, Validation Precision: 0.9459733713099105, Validation Recall: 0.8956965718453683, Validation F1 Score: 0.9189702321510603\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evaluating: 100%|██████████| 472/472 [00:25<00:00, 18.27it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Precision: 0.9516187544992264, Test Recall: 0.9015980372654333, Test F1 Score: 0.9247843555971894\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Store the model"
      ],
      "metadata": {
        "id": "MDsgmYZOHd1e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# After training is complete, save the model\n",
        "model.save_pretrained('/content/gdrive/MyDrive/TimePressure/resp_f_model/')\n",
        "\n",
        "# Save the tokenizer\n",
        "tokenizer.save_pretrained('/content/gdrive/MyDrive/TimePressure/resp_f_model/')"
      ],
      "metadata": {
        "id": "nh9cDazmHcaE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a7cc8f06-2e0d-41c4-b746-bd92e41ac13f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('/content/gdrive/MyDrive/TimePressure/resp_f_model/tokenizer_config.json',\n",
              " '/content/gdrive/MyDrive/TimePressure/resp_f_model/special_tokens_map.json',\n",
              " '/content/gdrive/MyDrive/TimePressure/resp_f_model/vocab.txt',\n",
              " '/content/gdrive/MyDrive/TimePressure/resp_f_model/added_tokens.json')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 9.2 Predicting out of sample invocations"
      ],
      "metadata": {
        "id": "J5f2yLUJoZ1j"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 9.2.1 Prediction in President and Home Affairs speeches"
      ],
      "metadata": {
        "id": "h9-nlUt_OXJE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import torch\n",
        "from transformers import BertTokenizer, BertForTokenClassification\n",
        "from tqdm import tqdm\n",
        "\n",
        "# Define paths\n",
        "model_path = '/content/gdrive/MyDrive/TimePressure/resp_f_model/'\n",
        "prediction_data_path = '/content/gdrive/MyDrive/TimePressure/migration_sample_combined2.feather'\n",
        "\n",
        "# Load the trained model\n",
        "model = BertForTokenClassification.from_pretrained(model_path)\n",
        "model.eval()\n",
        "\n",
        "# Load the tokenizer\n",
        "tokenizer = BertTokenizer.from_pretrained(model_path)\n",
        "\n",
        "# Load the new data\n",
        "prediction_df = pd.read_feather(prediction_data_path)\n",
        "\n",
        "# Define a function to prepare the new data\n",
        "def prepare_data(sentences, tokenizer, max_length):\n",
        "    input_ids = []\n",
        "    attention_masks = []\n",
        "\n",
        "    for sentence in sentences:\n",
        "        tokens = tokenizer.tokenize(sentence)\n",
        "        tokens = ['[CLS]'] + tokens + ['[SEP]']\n",
        "\n",
        "        ids = tokenizer.convert_tokens_to_ids(tokens)\n",
        "        ids = ids[:max_length] + [0] * (max_length - len(ids))\n",
        "\n",
        "        mask = [1] * len(tokens)\n",
        "        mask = mask[:max_length] + [0] * (max_length - len(mask))\n",
        "\n",
        "        input_ids.append(ids)\n",
        "        attention_masks.append(mask)\n",
        "\n",
        "    return torch.tensor(input_ids), torch.tensor(attention_masks)\n",
        "\n",
        "# Prepare for batch processing\n",
        "batch_size = 64\n",
        "max_length = 128\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "model.to(device)\n",
        "\n",
        "# List to collect all predicted labels for each sentence\n",
        "all_predicted_labels = []\n",
        "\n",
        "# Process each batch with a progress bar\n",
        "with tqdm(total=len(prediction_df), desc=\"Predicting\", unit=\"sentence\") as pbar:\n",
        "    for i in range(0, len(prediction_df), batch_size):\n",
        "        batch_sentences = prediction_df['sentence'][i:i + batch_size]\n",
        "        input_ids, attention_masks = prepare_data(batch_sentences, tokenizer, max_length)\n",
        "\n",
        "        input_ids = input_ids.to(device)\n",
        "        attention_masks = attention_masks.to(device)\n",
        "\n",
        "        with torch.no_grad():\n",
        "            outputs = model(input_ids=input_ids, attention_mask=attention_masks)\n",
        "            logits = outputs.logits\n",
        "\n",
        "        # Convert logits to probabilities and get token-level predictions\n",
        "        batch_predicted_labels = torch.argmax(logits, dim=-1).cpu().numpy()\n",
        "\n",
        "        # Store token-level predictions for each sentence, excluding [CLS] and [SEP]\n",
        "        for j, tokens_labels in enumerate(batch_predicted_labels):\n",
        "            sentence_len = len(input_ids[j]) if input_ids[j][-1] == 0 else max_length  # Adjust for padding\n",
        "            tokens_labels = tokens_labels[1:sentence_len-1]  # Exclude [CLS] and [SEP]\n",
        "            all_predicted_labels.append(tokens_labels.tolist())\n",
        "\n",
        "        # Update progress bar\n",
        "        pbar.update(len(batch_sentences))\n",
        "\n",
        "# Assign the token-level predictions to the DataFrame as lists\n",
        "prediction_df['resp_f_token_pred'] = all_predicted_labels\n",
        "\n",
        "# Save the updated DataFrame\n",
        "updated_prediction_data_path = '/content/gdrive/MyDrive/TimePressure/migration_sample_with_predictions_e.feather'\n",
        "prediction_df.to_feather(updated_prediction_data_path)\n",
        "\n",
        "# Display the updated DataFrame\n",
        "print(prediction_df.head())\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0LnXNGldsP5k",
        "outputId": "5914fb4b-bc54-4f72-f9b0-a78d58296d06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Predicting: 100%|██████████| 74889/74889 [02:28<00:00, 505.58sentence/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   speech_id  sentence_id       date  \\\n",
            "0          0            1 2017-05-05   \n",
            "1          0            2 2017-05-05   \n",
            "2          0            3 2017-05-05   \n",
            "3          0            4 2017-05-05   \n",
            "4          0            5 2017-05-05   \n",
            "\n",
            "                                               title              speaker  \\\n",
            "0  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "1  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "2  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "3  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "4  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "\n",
            "   portfolio context                                             pdfurl  \\\n",
            "0  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "1  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "2  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "3  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "4  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "\n",
            "                                                 url text.annotated  ...  \\\n",
            "0  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "1  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "2  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "3  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "4  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "\n",
            "                                       sentence_text  \\\n",
            "0  Caro Antonio, Cari amici, Monsieur le Ministre...   \n",
            "1  I am hesitating between English and French, bu...   \n",
            "2  And also because the French will have election...   \n",
            "3  Mais avant de faire ça, je dois vous dire que ...   \n",
            "4  et donc je ne peux pas faire un discours magis...   \n",
            "\n",
            "                                    title_translated pdfonly     probs  \\\n",
            "0  ''With an outside view'' – Speech by President...   FALSE  0.785897   \n",
            "1  ''With an outside view'' – Speech by President...   FALSE  0.970456   \n",
            "2  ''With an outside view'' – Speech by President...   FALSE  0.991257   \n",
            "3  ''With an outside view'' – Speech by President...   FALSE  0.995607   \n",
            "4  ''With an outside view'' – Speech by President...   FALSE   0.99236   \n",
            "\n",
            "  langdetect_sentence                                sentence_translated  \\\n",
            "0                  en  Caro Antonio, Cari amici, Mr. Minister, Mr. Ma...   \n",
            "1                  en  I am hesitating between English and French, bu...   \n",
            "2                  en  And also because the French will have election...   \n",
            "3                  fr  But before I do this, I have to tell you that ...   \n",
            "4                  fr  And so I can't make a masterful speech, but I'...   \n",
            "\n",
            "                                    sentence_text_en  year  \\\n",
            "0  Caro Antonio, Cari amici, Mr. Minister, Mr. Ma...  2017   \n",
            "1  I am hesitating between English and French, bu...  2017   \n",
            "2  And also because the French will have election...  2017   \n",
            "3  But before I do this, I have to tell you that ...  2017   \n",
            "4  And so I can't make a masterful speech, but I'...  2017   \n",
            "\n",
            "                                            sentence  \\\n",
            "0  Caro Antonio, Cari amici, Mr. Minister, Mr. Ma...   \n",
            "1  I am hesitating between English and French, bu...   \n",
            "2  And also because the French will have election...   \n",
            "3  But before I do this, I have to tell you that ...   \n",
            "4  And so I can't make a masterful speech, but I'...   \n",
            "\n",
            "                                   resp_f_token_pred  \n",
            "0  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
            "1  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
            "2  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
            "3  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
            "4  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
            "\n",
            "[5 rows x 23 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(prediction_df['resp_f_token_pred'].head(100))  # View the first 10 entries\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "obyXCwuyGIms",
        "outputId": "e276f160-e8d1-492b-b949-b9c7466229e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
            "1     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
            "2     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
            "3     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
            "4     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
            "                            ...                        \n",
            "95    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
            "96    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
            "97    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
            "98    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
            "99    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
            "Name: resp_f_token_pred, Length: 100, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 9.2.2 Aggregation to sentence level predicitions"
      ],
      "metadata": {
        "id": "Dg65pzlaPR9p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from tqdm import tqdm\n",
        "\n",
        "# Initialize columns for the three aggregation methods\n",
        "prediction_df['resp_f_sentence_pred_method1'] = 0  # Method 1: Three-token rule\n",
        "prediction_df['resp_f_sentence_pred_majority'] = 0  # Method 2: Majority vote\n",
        "prediction_df['resp_f_sentence_pred_first_non_negative'] = 0  # Method 3: First non-negative\n",
        "\n",
        "# Convert non-list entries in `resp_f_token_pred` to empty lists\n",
        "prediction_df['resp_f_token_pred'] = prediction_df['resp_f_token_pred'].apply(lambda x: x if isinstance(x, list) else [])\n",
        "\n",
        "# Method 1: Three-token rule\n",
        "for sentence, group in tqdm(prediction_df.groupby('sentence'), desc=\"Processing Method 1 (Three-token rule)\"):\n",
        "    token_predictions = [item for sublist in group['resp_f_token_pred'] for item in sublist]\n",
        "    num_positive_tokens = sum(pred == 1 for pred in token_predictions)\n",
        "    prediction_df.loc[group.index, 'resp_f_sentence_pred_method1'] = 1 if num_positive_tokens >= 3 else 0\n",
        "\n",
        "# Method 2: Majority vote\n",
        "for sentence, group in tqdm(prediction_df.groupby('sentence'), desc=\"Processing Method 2 (Majority vote)\"):\n",
        "    token_predictions = [item for sublist in group['resp_f_token_pred'] for item in sublist]\n",
        "    if token_predictions:\n",
        "        majority_label = max(set(token_predictions), key=token_predictions.count)\n",
        "    else:\n",
        "        majority_label = -100\n",
        "    prediction_df.loc[group.index, 'resp_f_sentence_pred_majority'] = majority_label\n",
        "\n",
        "# Method 3: First non-negative label\n",
        "for sentence, group in tqdm(prediction_df.groupby('sentence'), desc=\"Processing Method 3 (First non-negative rule)\"):\n",
        "    token_predictions = [item for sublist in group['resp_f_token_pred'] for item in sublist]\n",
        "    first_non_negative = next((pred for pred in token_predictions if pred != -100), -100)\n",
        "    prediction_df.loc[group.index, 'resp_f_sentence_pred_first_non_negative'] = first_non_negative\n",
        "\n",
        "# Calculate prevalence of positive labels for each method\n",
        "method1_positive_count = (prediction_df['resp_f_sentence_pred_method1'] == 1).sum()\n",
        "method2_positive_count = (prediction_df['resp_f_sentence_pred_majority'] == 1).sum()\n",
        "method3_positive_count = (prediction_df['resp_f_sentence_pred_first_non_negative'] == 1).sum()\n",
        "total_sentences = prediction_df['sentence'].nunique()\n",
        "\n",
        "method1_prevalence = method1_positive_count / total_sentences * 100\n",
        "method2_prevalence = method2_positive_count / total_sentences * 100\n",
        "method3_prevalence = method3_positive_count / total_sentences * 100\n",
        "\n",
        "# Display prevalence results\n",
        "print(f\"Method 1 (Three-token rule) Prevalence: {method1_prevalence:.2f}%\")\n",
        "print(f\"Method 2 (Majority vote) Prevalence: {method2_prevalence:.2f}%\")\n",
        "print(f\"Method 3 (First non-negative rule) Prevalence: {method3_prevalence:.2f}%\")\n",
        "\n",
        "# Calculate correlations between aggregation methods\n",
        "correlation_matrix = prediction_df[['resp_f_sentence_pred_method1', 'resp_f_sentence_pred_majority', 'resp_f_sentence_pred_first_non_negative']].corr()\n",
        "\n",
        "# Display correlation results\n",
        "print(\"\\nCorrelation between aggregation methods:\")\n",
        "print(correlation_matrix)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D1ZYAsYyFSeI",
        "outputId": "51302825-0a98-4c54-bca8-fd4d99638a20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processing Method 1 (Three-token rule): 100%|██████████| 71364/71364 [00:42<00:00, 1668.06it/s]\n",
            "Processing Method 2 (Majority vote): 100%|██████████| 71364/71364 [00:42<00:00, 1696.08it/s]\n",
            "Processing Method 3 (First non-negative rule): 100%|██████████| 71364/71364 [00:42<00:00, 1697.24it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Method 1 (Three-token rule) Prevalence: 6.87%\n",
            "Method 2 (Majority vote) Prevalence: 3.81%\n",
            "Method 3 (First non-negative rule) Prevalence: 2.29%\n",
            "\n",
            "Correlation between aggregation methods:\n",
            "                                         resp_f_sentence_pred_method1  \\\n",
            "resp_f_sentence_pred_method1                                 1.000000   \n",
            "resp_f_sentence_pred_majority                                0.732993   \n",
            "resp_f_sentence_pred_first_non_negative                      0.561738   \n",
            "\n",
            "                                         resp_f_sentence_pred_majority  \\\n",
            "resp_f_sentence_pred_method1                                  0.732993   \n",
            "resp_f_sentence_pred_majority                                 1.000000   \n",
            "resp_f_sentence_pred_first_non_negative                       0.722469   \n",
            "\n",
            "                                         resp_f_sentence_pred_first_non_negative  \n",
            "resp_f_sentence_pred_method1                                            0.561738  \n",
            "resp_f_sentence_pred_majority                                           0.722469  \n",
            "resp_f_sentence_pred_first_non_negative                                 1.000000  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the updated DataFrame with sentence-level predictions\n",
        "output_path = '/content/gdrive/MyDrive/TimePressure/migration_sample_with_sentence_predictions_e.feather'\n",
        "prediction_df.to_feather(output_path)\n",
        "\n",
        "print(f\"DataFrame with predictions saved to: {output_path}\")\n",
        "# Display the updated DataFrame\n",
        "print(prediction_df.head())"
      ],
      "metadata": {
        "id": "ebUh4pWdiqf_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0786f321-9dfa-40b0-ff1e-ea4b8abf18cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DataFrame with predictions saved to: /content/gdrive/MyDrive/TimePressure/migration_sample_with_sentence_predictions_e.feather\n",
            "   speech_id  sentence_id       date  \\\n",
            "0          0            1 2017-05-05   \n",
            "1          0            2 2017-05-05   \n",
            "2          0            3 2017-05-05   \n",
            "3          0            4 2017-05-05   \n",
            "4          0            5 2017-05-05   \n",
            "\n",
            "                                               title              speaker  \\\n",
            "0  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "1  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "2  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "3  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "4  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "\n",
            "   portfolio context                                             pdfurl  \\\n",
            "0  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "1  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "2  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "3  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "4  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "\n",
            "                                                 url text.annotated  ...  \\\n",
            "0  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "1  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "2  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "3  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "4  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "\n",
            "      probs  langdetect_sentence  \\\n",
            "0  0.785897                   en   \n",
            "1  0.970456                   en   \n",
            "2  0.991257                   en   \n",
            "3  0.995607                   fr   \n",
            "4   0.99236                   fr   \n",
            "\n",
            "                                 sentence_translated  \\\n",
            "0  Caro Antonio, Cari amici, Mr. Minister, Mr. Ma...   \n",
            "1  I am hesitating between English and French, bu...   \n",
            "2  And also because the French will have election...   \n",
            "3  But before I do this, I have to tell you that ...   \n",
            "4  And so I can't make a masterful speech, but I'...   \n",
            "\n",
            "                                    sentence_text_en  year  \\\n",
            "0  Caro Antonio, Cari amici, Mr. Minister, Mr. Ma...  2017   \n",
            "1  I am hesitating between English and French, bu...  2017   \n",
            "2  And also because the French will have election...  2017   \n",
            "3  But before I do this, I have to tell you that ...  2017   \n",
            "4  And so I can't make a masterful speech, but I'...  2017   \n",
            "\n",
            "                                            sentence  \\\n",
            "0  Caro Antonio, Cari amici, Mr. Minister, Mr. Ma...   \n",
            "1  I am hesitating between English and French, bu...   \n",
            "2  And also because the French will have election...   \n",
            "3  But before I do this, I have to tell you that ...   \n",
            "4  And so I can't make a masterful speech, but I'...   \n",
            "\n",
            "                                   resp_f_token_pred  \\\n",
            "0  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "1  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "2  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "3  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "4  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "\n",
            "  resp_f_sentence_pred_method1 resp_f_sentence_pred_majority  \\\n",
            "0                            0                             0   \n",
            "1                            0                             0   \n",
            "2                            0                             0   \n",
            "3                            0                             0   \n",
            "4                            0                             0   \n",
            "\n",
            "  resp_f_sentence_pred_first_non_negative  \n",
            "0                                       0  \n",
            "1                                       0  \n",
            "2                                       0  \n",
            "3                                       0  \n",
            "4                                       0  \n",
            "\n",
            "[5 rows x 26 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 9.2.3 Visualize predicitions"
      ],
      "metadata": {
        "id": "9VxHhWZlsUE6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Load the saved DataFrame\n",
        "output_path = '/content/gdrive/MyDrive/TimePressure/migration_sample_with_sentence_predictions_e.feather'\n",
        "prediction_df = pd.read_feather(output_path)\n",
        "\n",
        "# Display all column names\n",
        "print(\"Columns in the saved DataFrame:\")\n",
        "print(prediction_df.columns.tolist())\n",
        "\n",
        "# Optionally, display the first few rows to see sample data\n",
        "print(\"\\nSample data from the saved DataFrame:\")\n",
        "print(prediction_df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ad44-_jjRUCi",
        "outputId": "c06fe03c-eeb7-4909-f3a6-f739ed677045"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Columns in the saved DataFrame:\n",
            "['speech_id', 'sentence_id', 'date', 'title', 'speaker', 'portfolio', 'context', 'pdfurl', 'url', 'text.annotated', 'after.sampling', 'length', 'langdetect', 'sentence_text', 'title_translated', 'pdfonly', 'probs', 'langdetect_sentence', 'sentence_translated', 'sentence_text_en', 'year', 'sentence', 'resp_f_token_pred', 'resp_f_sentence_pred_method1', 'resp_f_sentence_pred_majority', 'resp_f_sentence_pred_first_non_negative']\n",
            "\n",
            "Sample data from the saved DataFrame:\n",
            "   speech_id  sentence_id       date  \\\n",
            "0          0            1 2017-05-05   \n",
            "1          0            2 2017-05-05   \n",
            "2          0            3 2017-05-05   \n",
            "3          0            4 2017-05-05   \n",
            "4          0            5 2017-05-05   \n",
            "\n",
            "                                               title              speaker  \\\n",
            "0  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "1  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "2  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "3  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "4  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "\n",
            "   portfolio context                                             pdfurl  \\\n",
            "0  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "1  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "2  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "3  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "4  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "\n",
            "                                                 url text.annotated  ...  \\\n",
            "0  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "1  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "2  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "3  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "4  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "\n",
            "      probs  langdetect_sentence  \\\n",
            "0  0.785897                   en   \n",
            "1  0.970456                   en   \n",
            "2  0.991257                   en   \n",
            "3  0.995607                   fr   \n",
            "4   0.99236                   fr   \n",
            "\n",
            "                                 sentence_translated  \\\n",
            "0  Caro Antonio, Cari amici, Mr. Minister, Mr. Ma...   \n",
            "1  I am hesitating between English and French, bu...   \n",
            "2  And also because the French will have election...   \n",
            "3  But before I do this, I have to tell you that ...   \n",
            "4  And so I can't make a masterful speech, but I'...   \n",
            "\n",
            "                                    sentence_text_en  year  \\\n",
            "0  Caro Antonio, Cari amici, Mr. Minister, Mr. Ma...  2017   \n",
            "1  I am hesitating between English and French, bu...  2017   \n",
            "2  And also because the French will have election...  2017   \n",
            "3  But before I do this, I have to tell you that ...  2017   \n",
            "4  And so I can't make a masterful speech, but I'...  2017   \n",
            "\n",
            "                                            sentence  \\\n",
            "0  Caro Antonio, Cari amici, Mr. Minister, Mr. Ma...   \n",
            "1  I am hesitating between English and French, bu...   \n",
            "2  And also because the French will have election...   \n",
            "3  But before I do this, I have to tell you that ...   \n",
            "4  And so I can't make a masterful speech, but I'...   \n",
            "\n",
            "                                   resp_f_token_pred  \\\n",
            "0  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "1  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "2  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "3  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "4  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "\n",
            "  resp_f_sentence_pred_method1 resp_f_sentence_pred_majority  \\\n",
            "0                            0                             0   \n",
            "1                            0                             0   \n",
            "2                            0                             0   \n",
            "3                            0                             0   \n",
            "4                            0                             0   \n",
            "\n",
            "  resp_f_sentence_pred_first_non_negative  \n",
            "0                                       0  \n",
            "1                                       0  \n",
            "2                                       0  \n",
            "3                                       0  \n",
            "4                                       0  \n",
            "\n",
            "[5 rows x 26 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "# Assuming prediction_df is already loaded and contains 'date', 'resp_f_sentence_pred_first_non_negative', and 'portfolio' columns\n",
        "\n",
        "# Ensure the date column is in datetime format\n",
        "prediction_df['date'] = pd.to_datetime(prediction_df['date'])\n",
        "\n",
        "# Extract the year from the date column\n",
        "prediction_df['year'] = prediction_df['date'].dt.year\n",
        "\n",
        "# Calculate positive sentences per year for 'President' portfolio\n",
        "president_df = prediction_df[prediction_df['portfolio'] == 'President']\n",
        "positive_sentences_president = president_df.groupby('year')['resp_f_sentence_pred_first_non_negative'].sum()\n",
        "unique_speech_ids_president = president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Calculate positive sentences per year for non-'President' portfolios\n",
        "non_president_df = prediction_df[prediction_df['portfolio'] != 'President']\n",
        "positive_sentences_non_president = non_president_df.groupby('year')['resp_f_sentence_pred_first_non_negative'].sum()\n",
        "unique_speech_ids_non_president = non_president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Calculate combined positive sentences per year\n",
        "combined_sentences_per_year = positive_sentences_president + positive_sentences_non_president\n",
        "\n",
        "# Plot the results\n",
        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "\n",
        "\n",
        "# Plot combined line\n",
        "ax1.plot(combined_sentences_per_year.index, combined_sentences_per_year.values, marker='o', color='black', linestyle='-', label='Explicit time pressure invocations')\n",
        "\n",
        "# Set y-axis limits for ax1\n",
        "ax1.set_ylim(0, 400)\n",
        "\n",
        "# Bar plot for unique speech_id for both 'President' and non-'President' portfolios\n",
        "ax2 = ax1.twinx()\n",
        "ax2.bar(unique_speech_ids_president.index, unique_speech_ids_president.values, width=0.4, align='center', color='lightblue', alpha=0.5, label='Speeches President')\n",
        "ax2.bar(unique_speech_ids_non_president.index + 0.4, unique_speech_ids_non_president.values, width=0.4, align='center', color='lightgrey', alpha=0.5, label='Speeches Commissioner')\n",
        "ax2.set_ylabel('Number of speeches', color='gray')\n",
        "ax2.tick_params(axis='y', labelcolor='gray')\n",
        "\n",
        "# Add vertical lines with Commissioner tenure labels and move them further below\n",
        "events = {\n",
        "    '1995': 'Gradin',\n",
        "    '1999': 'Vitorino',\n",
        "    '2004': 'Fratini',\n",
        "    '2008': 'Barrot',\n",
        "    '2010': 'Malmström',\n",
        "    '2014': 'Avramopoulos',\n",
        "    '2019': 'Johansson'\n",
        "}\n",
        "\n",
        "# Plot vertical lines for each event and move the labels lower to avoid overlap with the legend\n",
        "for year, name in events.items():\n",
        "    ax1.axvline(x=int(year), color='darkgrey', linestyle='--', lw=1)\n",
        "    ax1.text(int(year), ax1.get_ylim()[0] + 200, name, color='darkgrey', rotation=90, verticalalignment='bottom', horizontalalignment='center')\n",
        "\n",
        "# Set x-axis to show every year and rotate labels vertically\n",
        "ax1.set_xticks(range(prediction_df['year'].min(), prediction_df['year'].max() + 1))\n",
        "ax1.set_xticklabels(range(prediction_df['year'].min(), prediction_df['year'].max() + 1), rotation=90)\n",
        "\n",
        "# Add legend\n",
        "lines, labels = ax1.get_legend_handles_labels()\n",
        "lines2, labels2 = ax2.get_legend_handles_labels()\n",
        "ax1.legend(lines + lines2, labels + labels2, loc='upper left')\n",
        "\n",
        "# Adjust layout\n",
        "fig.tight_layout()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "ycqcgJV6Ll1J",
        "outputId": "3a5282ac-3598-47dd-d3e9-ec406134368d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "# Load the asylum data and prediction data\n",
        "asylum_path = '/content/gdrive/MyDrive/TimePressure/asylum_year.xlsx'\n",
        "asylum_df = pd.read_excel(asylum_path)\n",
        "\n",
        "# Rename and scale the asylum data for visibility\n",
        "asylum_df.rename(columns={'asylum_nr_year': 'asylum_total'}, inplace=True)\n",
        "asylum_df['asylum_total_scaled'] = asylum_df['asylum_total'] / 10000\n",
        "\n",
        "# Assuming prediction_df is already loaded and contains 'date', 'resp_f_sentence_pred_first_non_negative', and 'portfolio' columns\n",
        "prediction_df['date'] = pd.to_datetime(prediction_df['date'])\n",
        "prediction_df['year'] = prediction_df['date'].dt.year\n",
        "\n",
        "# Calculate positive sentences per year for 'President' portfolio\n",
        "president_df = prediction_df[prediction_df['portfolio'] == 'President']\n",
        "positive_sentences_president = president_df.groupby('year')['resp_f_sentence_pred_first_non_negative'].sum()\n",
        "unique_speech_ids_president = president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Calculate positive sentences per year for non-'President' portfolios\n",
        "non_president_df = prediction_df[prediction_df['portfolio'] != 'President']\n",
        "positive_sentences_non_president = non_president_df.groupby('year')['resp_f_sentence_pred_first_non_negative'].sum()\n",
        "unique_speech_ids_non_president = non_president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Calculate combined positive sentences per year\n",
        "combined_sentences_per_year = positive_sentences_president + positive_sentences_non_president\n",
        "\n",
        "# Plot the results\n",
        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "# Plot combined line for explicit time pressure invocations\n",
        "ax1.plot(combined_sentences_per_year.index, combined_sentences_per_year.values, marker='o', color='black', linestyle='-', label='Explicit time pressure invocations')\n",
        "\n",
        "# Plot scaled asylum numbers on the same axis\n",
        "ax1.plot(asylum_df['year'], asylum_df['asylum_total_scaled'], color='darkgrey', marker='s', linestyle='-', label='Yearly European Asylum Numbers (10,000)')\n",
        "ax1.set_ylim(0, 400)\n",
        "ax1.set_ylabel('Explicit Invocations of Time Pressure', color='black')\n",
        "\n",
        "# Bar plot for unique speech_id for both 'President' and non-'President' portfolios\n",
        "ax2 = ax1.twinx()\n",
        "ax2.bar(unique_speech_ids_president.index, unique_speech_ids_president.values, width=0.4, align='center', color='lightblue', alpha=0.5, label='Speeches President')\n",
        "ax2.bar(unique_speech_ids_non_president.index + 0.4, unique_speech_ids_non_president.values, width=0.4, align='center', color='lightgrey', alpha=0.5, label='Speeches Commissioner')\n",
        "ax2.set_ylabel('Number of speeches', color='gray')\n",
        "ax2.tick_params(axis='y', labelcolor='gray')\n",
        "\n",
        "# Add vertical lines with Commissioner tenure labels and position labels lower\n",
        "events = {\n",
        "    1995: 'Gradin',\n",
        "    1999: 'Vitorino',\n",
        "    2004: 'Fratini',\n",
        "    2008: 'Barrot',\n",
        "    2010: 'Malmström',\n",
        "    2014: 'Avramopoulos',\n",
        "    2019: 'Johansson'\n",
        "}\n",
        "\n",
        "for year, name in events.items():\n",
        "    ax1.axvline(x=year, color='darkgrey', linestyle='--', lw=1)\n",
        "    ax1.text(year, ax1.get_ylim()[0] + 200, name, color='darkgrey', rotation=90, verticalalignment='bottom', horizontalalignment='center')\n",
        "\n",
        "# Set x-axis to show every year and rotate labels vertically\n",
        "ax1.set_xticks(range(prediction_df['year'].min(), prediction_df['year'].max() + 1))\n",
        "ax1.set_xticklabels(range(prediction_df['year'].min(), prediction_df['year'].max() + 1), rotation=90)\n",
        "\n",
        "# Add legend\n",
        "lines, labels = ax1.get_legend_handles_labels()\n",
        "lines2, labels2 = ax2.get_legend_handles_labels()\n",
        "ax1.legend(lines + lines2, labels + labels2, loc='upper left')\n",
        "\n",
        "# Adjust layout\n",
        "fig.tight_layout()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "6kDsuhyMolwZ",
        "outputId": "ed94974a-f334-47b9-9a58-9854336da13b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "# Assuming prediction_df is already loaded and contains 'date', 'resp_f_sentence_predh', and 'portfolio' columns\n",
        "\n",
        "# Ensure the date column is in datetime format\n",
        "prediction_df['date'] = pd.to_datetime(prediction_df['date'])\n",
        "\n",
        "# Extract the year from the date column\n",
        "prediction_df['year'] = prediction_df['date'].dt.year\n",
        "\n",
        "# Calculate positive sentences per year for 'President' portfolio\n",
        "president_df = prediction_df[prediction_df['portfolio'] == 'President']\n",
        "positive_sentences_president = president_df.groupby('year')['resp_f_sentence_pred_first_non_negative'].sum()\n",
        "unique_speech_ids_president = president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Calculate positive sentences per year for non-'President' portfolios\n",
        "non_president_df = prediction_df[prediction_df['portfolio'] != 'President']\n",
        "positive_sentences_non_president = non_president_df.groupby('year')['resp_f_sentence_pred_first_non_negative'].sum()\n",
        "unique_speech_ids_non_president = non_president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Calculate combined positive sentences per year\n",
        "combined_sentences_per_year = positive_sentences_president + positive_sentences_non_president\n",
        "\n",
        "# Plot the results\n",
        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "# Plot line for 'President' portfolio\n",
        "ax1.plot(positive_sentences_president.index, positive_sentences_president.values, marker='o', color='grey', linestyle='-', label='President')\n",
        "ax1.set_ylabel('Number of Explicit Time Pressure Invocations', color='black')\n",
        "ax1.tick_params(axis='y', labelcolor='blue')\n",
        "\n",
        "# Plot line for non-'President' portfolios\n",
        "ax1.plot(positive_sentences_non_president.index, positive_sentences_non_president.values, marker='o', color='grey', linestyle='--', label='Commissioner')\n",
        "ax1.tick_params(axis='y', labelcolor='black')\n",
        "\n",
        "# Plot combined line\n",
        "ax1.plot(combined_sentences_per_year.index, combined_sentences_per_year.values, marker='o', color='black', linestyle='-', label='Combined')\n",
        "# Set y-axis limits for ax1\n",
        "ax1.set_ylim(0, 1500)\n",
        "\n",
        "# Bar plot for unique speech_id for both 'President' and non-'President' portfolios\n",
        "ax2 = ax1.twinx()\n",
        "ax2.bar(unique_speech_ids_president.index, unique_speech_ids_president.values, width=0.4, align='center', color='lightblue', alpha=0.5, label='Speeches President')\n",
        "ax2.bar(unique_speech_ids_non_president.index + 0.4, unique_speech_ids_non_president.values, width=0.4, align='center', color='lightgrey', alpha=0.5, label='Speeches Commissioner')\n",
        "ax2.set_ylabel('Number of speeches', color='gray')\n",
        "ax2.tick_params(axis='y', labelcolor='gray')\n",
        "\n",
        "# Set x-axis to show every year and rotate labels vertically\n",
        "ax1.set_xticks(range(prediction_df['year'].min(), prediction_df['year'].max() + 1))\n",
        "ax1.set_xticklabels(range(prediction_df['year'].min(), prediction_df['year'].max() + 1), rotation=90)\n",
        "\n",
        "# Add legend\n",
        "lines, labels = ax1.get_legend_handles_labels()\n",
        "lines2, labels2 = ax2.get_legend_handles_labels()\n",
        "ax1.legend(lines + lines2, labels + labels2, loc='upper left')\n",
        "\n",
        "# Adjust layout\n",
        "fig.tight_layout()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "LrYPVYfpjbm1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "87bc277d-8608-4044-ee23-866f81eb2983"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Draw sample for post stratification."
      ],
      "metadata": {
        "id": "3m2qWuCqp9ls"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Aggregate token-level predictions to get sentence-level predictions\n",
        "sentence_level_predictions = {}\n",
        "\n",
        "# Iterate over the DataFrame rows\n",
        "for index, row in prediction_df.iterrows():\n",
        "    sentence = row['sentence']\n",
        "    token_predictions = row['resp_f_token_predh']\n",
        "\n",
        "    # Count the number of positive token predictions\n",
        "    num_positive_tokens = sum(token_predictions)\n",
        "\n",
        "    # If at least three token predictions are positive, consider the entire sentence as positive\n",
        "    if num_positive_tokens >= 1:\n",
        "        sentence_level_predictions[sentence] = 1\n",
        "    else:\n",
        "        sentence_level_predictions[sentence] = 0\n",
        "\n",
        "# Add sentence-level predictions to the DataFrame\n",
        "prediction_df['resp_f_sentence_pred'] = prediction_df['sentence'].map(sentence_level_predictions)\n",
        "\n",
        "# Separate positive and negative predictions\n",
        "positive_sentences_resp_f = prediction_df[prediction_df['resp_f_sentence_pred'] == 1]\n",
        "negative_sentences_resp_f = prediction_df[prediction_df['resp_f_sentence_pred'] == 0]\n",
        "\n",
        "# Randomly sample 1000 positive and 1000 negative sentences\n",
        "sampled_positive_resp_f = positive_sentences_resp_f.sample(n=1000, random_state=42, replace=True)\n",
        "sampled_negative_resp_f = negative_sentences_resp_f.sample(n=1000, random_state=42, replace=True)\n",
        "\n",
        "# Combine the sampled data into a balanced dataset\n",
        "balanced_sample_resp_f_df = pd.concat([sampled_positive_resp_f, sampled_negative_resp_f])\n",
        "\n",
        "# Save the balanced dataset as an Excel file\n",
        "excel_output_path = '/content/gdrive/MyDrive/TimePressure/balanced_resp_f_sentence_sample.xlsx'\n",
        "\n",
        "# Convert to Excel and save\n",
        "balanced_sample_resp_f_df.to_excel(excel_output_path, index=False)\n",
        "\n",
        "# Confirm the save location\n",
        "print(f\"The balanced dataset has been saved to: {excel_output_path}\")"
      ],
      "metadata": {
        "id": "RwRrlsbBp7dq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d65a8369-77e6-4db5-f679-cef13e11b46e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The balanced dataset has been saved to: /content/gdrive/MyDrive/TimePressure/balanced_resp_f_sentence_sample.xlsx\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Graphing teh prevalence of time pressure"
      ],
      "metadata": {
        "id": "ad_yFlxSqq_N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "# Assuming prediction_df is already loaded and contains 'date', 'resp_f_sentence_predh', and 'portfolio' columns\n",
        "\n",
        "# Ensure the date column is in datetime format\n",
        "prediction_df['date'] = pd.to_datetime(prediction_df['date'])\n",
        "\n",
        "# Extract the year from the date column\n",
        "prediction_df['year'] = prediction_df['date'].dt.year\n",
        "\n",
        "# Calculate positive sentences per year for 'President' portfolio\n",
        "president_df = prediction_df[prediction_df['portfolio'] == 'President']\n",
        "positive_sentences_president = president_df.groupby('year')['resp_f_sentence_predh'].sum()\n",
        "unique_speech_ids_president = president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Calculate positive sentences per year for non-'President' portfolios\n",
        "non_president_df = prediction_df[prediction_df['portfolio'] != 'President']\n",
        "positive_sentences_non_president = non_president_df.groupby('year')['resp_f_sentence_predh'].sum()\n",
        "unique_speech_ids_non_president = non_president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Calculate combined positive sentences per year\n",
        "combined_sentences_per_year = positive_sentences_president + positive_sentences_non_president\n",
        "\n",
        "# Plot the results\n",
        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "# Plot line for 'President' portfolio\n",
        "ax1.plot(positive_sentences_president.index, positive_sentences_president.values, marker='o', color='grey', linestyle='-', label='President')\n",
        "ax1.set_ylabel('Number of Explicit Time Pressure Invocations', color='black')\n",
        "ax1.tick_params(axis='y', labelcolor='blue')\n",
        "\n",
        "# Plot line for non-'President' portfolios\n",
        "ax1.plot(positive_sentences_non_president.index, positive_sentences_non_president.values, marker='o', color='grey', linestyle='--', label='Commissioner')\n",
        "ax1.tick_params(axis='y', labelcolor='black')\n",
        "\n",
        "# Plot combined line\n",
        "ax1.plot(combined_sentences_per_year.index, combined_sentences_per_year.values, marker='o', color='black', linestyle='-', label='Combined')\n",
        "# Set y-axis limits for ax1\n",
        "ax1.set_ylim(0, 600)\n",
        "\n",
        "# Bar plot for unique speech_id for both 'President' and non-'President' portfolios\n",
        "ax2 = ax1.twinx()\n",
        "ax2.bar(unique_speech_ids_president.index, unique_speech_ids_president.values, width=0.4, align='center', color='lightblue', alpha=0.5, label='Speeches President')\n",
        "ax2.bar(unique_speech_ids_non_president.index + 0.4, unique_speech_ids_non_president.values, width=0.4, align='center', color='lightgrey', alpha=0.5, label='Speeches Commissioner')\n",
        "ax2.set_ylabel('Number of speeches', color='gray')\n",
        "ax2.tick_params(axis='y', labelcolor='gray')\n",
        "\n",
        "# Set x-axis to show every year and rotate labels vertically\n",
        "ax1.set_xticks(range(prediction_df['year'].min(), prediction_df['year'].max() + 1))\n",
        "ax1.set_xticklabels(range(prediction_df['year'].min(), prediction_df['year'].max() + 1), rotation=90)\n",
        "\n",
        "# Add legend\n",
        "lines, labels = ax1.get_legend_handles_labels()\n",
        "lines2, labels2 = ax2.get_legend_handles_labels()\n",
        "ax1.legend(lines + lines2, labels + labels2, loc='upper left')\n",
        "\n",
        "# Adjust layout\n",
        "fig.tight_layout()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "xsCTqrX0qF-4",
        "outputId": "410c3c90-0eb0-4058-d0d0-3f3e0c7b3341"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "# Assuming prediction_df is already loaded and contains 'date', 'resp_f_sentence_predh', and 'portfolio' columns\n",
        "\n",
        "# Ensure the date column is in datetime format\n",
        "prediction_df['date'] = pd.to_datetime(prediction_df['date'])\n",
        "\n",
        "# Extract the year from the date column\n",
        "prediction_df['year'] = prediction_df['date'].dt.year\n",
        "\n",
        "# Calculate positive sentences per year for 'President' portfolio\n",
        "president_df = prediction_df[prediction_df['portfolio'] == 'President']\n",
        "positive_sentences_president = president_df.groupby('year')['resp_f_sentence_predh'].sum()\n",
        "unique_speech_ids_president = president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Calculate positive sentences per year for non-'President' portfolios\n",
        "non_president_df = prediction_df[prediction_df['portfolio'] != 'President']\n",
        "positive_sentences_non_president = non_president_df.groupby('year')['resp_f_sentence_predh'].sum()\n",
        "unique_speech_ids_non_president = non_president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Calculate combined positive sentences per year\n",
        "combined_sentences_per_year = positive_sentences_president + positive_sentences_non_president\n",
        "\n",
        "# Plot the results\n",
        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "# Plot line for 'President' portfolio\n",
        "ax1.plot(positive_sentences_president.index, positive_sentences_president.values, marker='o', color='grey', linestyle='-', label='President')\n",
        "ax1.set_ylabel('Number of Explicit Time Pressure Invocations', color='black')\n",
        "ax1.tick_params(axis='y', labelcolor='blue')\n",
        "\n",
        "# Plot line for non-'President' portfolios\n",
        "ax1.plot(positive_sentences_non_president.index, positive_sentences_non_president.values, marker='o', color='grey', linestyle='--', label='Commissioner')\n",
        "ax1.tick_params(axis='y', labelcolor='black')\n",
        "\n",
        "# Plot combined line\n",
        "ax1.plot(combined_sentences_per_year.index, combined_sentences_per_year.values, marker='o', color='black', linestyle='-', label='Combined')\n",
        "\n",
        "# Set y-axis limits for ax1\n",
        "ax1.set_ylim(0, 300)\n",
        "\n",
        "# Bar plot for unique speech_id for both 'President' and non-'President' portfolios\n",
        "ax2 = ax1.twinx()\n",
        "ax2.bar(unique_speech_ids_president.index, unique_speech_ids_president.values, width=0.4, align='center', color='lightblue', alpha=0.5, label='Speeches President')\n",
        "ax2.bar(unique_speech_ids_non_president.index + 0.4, unique_speech_ids_non_president.values, width=0.4, align='center', color='lightgrey', alpha=0.5, label='Speeches Commissioner')\n",
        "ax2.set_ylabel('Number of speeches', color='gray')\n",
        "ax2.tick_params(axis='y', labelcolor='gray')\n",
        "\n",
        "# Add vertical lines with Commissioner tenure labels and move them further below\n",
        "events = {\n",
        "    '1995': 'Gradin',\n",
        "    '1999': 'Vitorino',\n",
        "    '2004': 'Fratini',\n",
        "    '2008': 'Barrot',\n",
        "    '2010': 'Malmström',\n",
        "    '2014': 'Avramopoulos',\n",
        "    '2019': 'Johansson'\n",
        "}\n",
        "\n",
        "# Plot vertical lines for each event and move the labels lower to avoid overlap with the legend\n",
        "for year, name in events.items():\n",
        "    ax1.axvline(x=int(year), color='darkgrey', linestyle='--', lw=1)\n",
        "    ax1.text(int(year), ax1.get_ylim()[0] + 200, name, color='darkgrey', rotation=90, verticalalignment='bottom', horizontalalignment='center')\n",
        "\n",
        "# Set x-axis to show every year and rotate labels vertically\n",
        "ax1.set_xticks(range(prediction_df['year'].min(), prediction_df['year'].max() + 1))\n",
        "ax1.set_xticklabels(range(prediction_df['year'].min(), prediction_df['year'].max() + 1), rotation=90)\n",
        "\n",
        "# Add legend\n",
        "lines, labels = ax1.get_legend_handles_labels()\n",
        "lines2, labels2 = ax2.get_legend_handles_labels()\n",
        "ax1.legend(lines + lines2, labels + labels2, loc='upper left')\n",
        "\n",
        "# Adjust layout\n",
        "fig.tight_layout()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "ZkbpTrMDwUM-",
        "outputId": "36cee68a-485c-4665-beb6-883b59967269"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hjZmuPIicqgy"
      },
      "source": [
        "# 10) Token level - implicit low time pressure"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 10.1 Load nd pre-process data"
      ],
      "metadata": {
        "id": "LHvrleaihT1L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import re\n",
        "from collections import defaultdict\n",
        "import torch\n",
        "from transformers import BertTokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "# Load the Excel file into a DataFrame\n",
        "excel_file_path = '/content/gdrive/MyDrive/TimePressure/multilabel_gold_all_final.xlsx'\n",
        "result_df = pd.read_excel(excel_file_path)\n",
        "\n",
        "# Initialize lists to store token-level data\n",
        "token_list = []\n",
        "label_list = []\n",
        "\n",
        "# Define a function to split a sentence into tokens\n",
        "def tokenize(sentence):\n",
        "    # Split the sentence into tokens (you may need to customize this based on your tokenization requirements)\n",
        "    return sentence.split()\n",
        "\n",
        "# Remove annotation markers for non-classified labels and create token-level data\n",
        "for _, row in result_df.iterrows():\n",
        "    # Convert the Sentence column to a string (if not already)\n",
        "    sentence = str(row['Sentence'])\n",
        "\n",
        "    # Check if tp_h label is not equal to 1\n",
        "    if row['tp_l'] != 1:\n",
        "        # Remove square brackets and their contents from the Sentence column\n",
        "        sentence = re.sub(r'\\[[^\\]]*\\]', '', sentence)\n",
        "\n",
        "    # Split the cleaned sentence into tokens\n",
        "    tokens = tokenize(sentence)\n",
        "\n",
        "    # Initialize a dictionary to store token-level labels\n",
        "    token_labels = defaultdict(int)\n",
        "\n",
        "    # Find the start and end indices of [s] and [tp_h] markers\n",
        "    start_index = sentence.find('[s]')\n",
        "    end_index = sentence.find('[tp_l]')\n",
        "\n",
        "    # Check if both start and end markers are found\n",
        "    if start_index != -1 and end_index != -1:\n",
        "        # Extract the token span between start and end indices\n",
        "        token_span = tokenize(sentence[start_index + len('[s]'):end_index])\n",
        "\n",
        "        # Assign label 1 to tokens within the span, 0 otherwise\n",
        "        for token in token_span:\n",
        "            token_labels[token] = 1\n",
        "\n",
        "    # Append token-label pairs to the lists\n",
        "    for token in tokens:\n",
        "        token_list.append(token)\n",
        "        label_list.append(token_labels[token])\n",
        "\n",
        "# Create a new DataFrame to store token-level data\n",
        "token_df = pd.DataFrame({'Token': token_list, 'Label': label_list})\n",
        "\n",
        "# Save the token-level DataFrame to a new Excel file\n",
        "token_df.to_excel('token_level_data.xlsx', index=False)\n",
        "\n",
        "# Load the token-level DataFrame\n",
        "token_df = pd.read_excel('token_level_data.xlsx')\n",
        "\n",
        "# Tokenizer\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "\n",
        "# Initialize lists to store token-level data and labels\n",
        "tokenized_texts = []\n",
        "labels = []\n",
        "\n",
        "# Iterate through each row of the token-level DataFrame\n",
        "for _, row in token_df.iterrows():\n",
        "    # Tokenize the token using the BERT tokenizer\n",
        "    tokens = tokenizer.tokenize(row['Token'])\n",
        "\n",
        "    # Add special tokens [CLS] and [SEP]\n",
        "    tokens = ['[CLS]'] + tokens + ['[SEP]']\n",
        "\n",
        "    # Add token IDs to the list of token-level data\n",
        "    token_ids = tokenizer.convert_tokens_to_ids(tokens)\n",
        "    tokenized_texts.append(token_ids)\n",
        "\n",
        "    # Add labels to the list of labels\n",
        "    labels.append(row['Label'])\n",
        "\n",
        "# Pad sequences to the maximum length\n",
        "max_len = max(len(seq) for seq in tokenized_texts)\n",
        "input_ids = pad_sequences(tokenized_texts, maxlen=max_len, dtype=\"long\", value=0, truncating=\"post\", padding=\"post\")\n",
        "\n",
        "# Convert lists to PyTorch tensors\n",
        "input_ids = torch.tensor(input_ids)\n",
        "labels = torch.tensor(labels)\n",
        "\n",
        "# Print the first few tokenized texts and labels\n",
        "print(\"Tokenized Texts:\", input_ids[:5])\n",
        "print(\"Labels:\", labels[:5])\n",
        "\n",
        "# Save the preprocessed DataFrame to a new Excel file\n",
        "result_df.to_excel('preprocessed_data.xlsx', index=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jo4cHFbxhJ2U",
        "outputId": "fb7f524e-cf6e-43a8-cad3-fdda7eb87b2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tokenized Texts: tensor([[  101,  2720,  1010,   102,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0],\n",
            "        [  101,  3472,  1010,   102,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0],\n",
            "        [  101,  6456,  1010,   102,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0],\n",
            "        [  101, 11218,   102,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0],\n",
            "        [  101,  1998,   102,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0]])\n",
            "Labels: tensor([0, 0, 0, 0, 0])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 10.1.2 Run BERT model implicit low (tp_l)"
      ],
      "metadata": {
        "id": "N3IfADb1h2Wy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from transformers import BertTokenizer, BertForTokenClassification, AdamW, get_linear_schedule_with_warmup\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score\n",
        "from tqdm import tqdm\n",
        "from sklearn.model_selection import train_test_split\n",
        "import numpy as np\n",
        "\n",
        "# Load the token-level DataFrame\n",
        "token_df = pd.read_excel('token_level_data.xlsx')\n",
        "\n",
        "# Tokenizer\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "\n",
        "# Define your token-level dataset\n",
        "class TokenDataset(Dataset):\n",
        "    def __init__(self, tokenizer, token_df, max_length):\n",
        "        self.tokenizer = tokenizer\n",
        "        self.token_df = token_df\n",
        "        self.max_length = max_length\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.token_df)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        tokens = self.tokenizer.tokenize(self.token_df.loc[idx, 'Token'])\n",
        "        tokens = ['[CLS]'] + tokens + ['[SEP]']\n",
        "\n",
        "        input_ids = self.tokenizer.convert_tokens_to_ids(tokens)\n",
        "        input_ids = input_ids[:self.max_length] + [0] * (self.max_length - len(input_ids))\n",
        "\n",
        "        attention_mask = [1] * len(tokens)\n",
        "        attention_mask = attention_mask[:self.max_length] + [0] * (self.max_length - len(attention_mask))\n",
        "\n",
        "        label = self.token_df.loc[idx, 'Label']\n",
        "        label = [label] + [-100] * (self.max_length - 1)  # Pad labels with -100 for ignored tokens\n",
        "\n",
        "        return {\n",
        "            'input_ids': torch.tensor(input_ids),\n",
        "            'attention_mask': torch.tensor(attention_mask),\n",
        "            'labels': torch.tensor(label)\n",
        "        }\n",
        "\n",
        "# Custom collate function to handle padding\n",
        "def custom_collate_fn(batch):\n",
        "    input_ids = torch.stack([item['input_ids'] for item in batch])\n",
        "    attention_masks = torch.stack([item['attention_mask'] for item in batch])\n",
        "    labels = torch.stack([item['labels'] for item in batch])\n",
        "\n",
        "    return input_ids, attention_masks, labels\n",
        "\n",
        "# Calculate class weights\n",
        "class_counts = token_df['Label'].value_counts().to_dict()\n",
        "total_samples = len(token_df)\n",
        "class_weights = {cls: total_samples/count for cls, count in class_counts.items()}\n",
        "\n",
        "weights = [class_weights[cls] for cls in sorted(class_weights.keys())]\n",
        "weights = torch.tensor(weights, dtype=torch.float)\n",
        "\n",
        "# Define your training function\n",
        "def train_model(model, train_loader, val_loader, criterion, optimizer, scheduler, epochs):\n",
        "    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "    model.to(device)\n",
        "    criterion = criterion.to(device)\n",
        "\n",
        "    best_val_f1 = 0\n",
        "\n",
        "    for epoch in range(epochs):\n",
        "        model.train()\n",
        "        total_loss = 0.0\n",
        "\n",
        "        for batch in tqdm(train_loader, desc=f\"Training Epoch {epoch+1}/{epochs}\"):\n",
        "            input_ids, attention_masks, labels = batch\n",
        "            input_ids = input_ids.to(device)\n",
        "            attention_masks = attention_masks.to(device)\n",
        "            labels = labels.to(device)\n",
        "\n",
        "            optimizer.zero_grad()\n",
        "            outputs = model(input_ids=input_ids, attention_mask=attention_masks)\n",
        "            logits = outputs.logits\n",
        "\n",
        "            logits = logits.view(-1, model.config.num_labels)\n",
        "            labels = labels.view(-1)\n",
        "\n",
        "            loss = criterion(logits, labels)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            scheduler.step()\n",
        "\n",
        "            total_loss += loss.item()\n",
        "\n",
        "        avg_loss = total_loss / len(train_loader)\n",
        "        val_precision, val_recall, val_f1 = evaluate_model(model, val_loader, criterion)\n",
        "        print(f\"Epoch {epoch+1}/{epochs}, Loss: {avg_loss}, Validation Precision: {val_precision}, Validation Recall: {val_recall}, Validation F1 Score: {val_f1}\")\n",
        "\n",
        "        # Save model if it has improved\n",
        "        if val_f1 > best_val_f1:\n",
        "            best_val_f1 = val_f1\n",
        "            torch.save(model.state_dict(), 'best_model.pt')\n",
        "\n",
        "def evaluate_model(model, val_loader, criterion):\n",
        "    model.eval()\n",
        "    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "    all_preds = []\n",
        "    all_labels = []\n",
        "    total_loss = 0.0\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for batch in tqdm(val_loader, desc=\"Evaluating\"):\n",
        "            input_ids, attention_masks, labels = batch\n",
        "            input_ids = input_ids.to(device)\n",
        "            attention_masks = attention_masks.to(device)\n",
        "            labels = labels.to(device)\n",
        "\n",
        "            outputs = model(input_ids=input_ids, attention_mask=attention_masks)\n",
        "            logits = outputs.logits\n",
        "\n",
        "            logits = logits.view(-1, model.config.num_labels)\n",
        "            labels = labels.view(-1)\n",
        "\n",
        "            loss = criterion(logits, labels)\n",
        "            total_loss += loss.item()\n",
        "\n",
        "            preds = logits.argmax(dim=-1).cpu().numpy().flatten()\n",
        "            label_ids = labels.cpu().numpy().flatten()\n",
        "\n",
        "            mask = label_ids != -100  # Ignore padded labels\n",
        "            all_preds.extend(preds[mask].tolist())\n",
        "            all_labels.extend(label_ids[mask].tolist())\n",
        "\n",
        "    avg_loss = total_loss / len(val_loader)\n",
        "    precision = precision_score(all_labels, all_preds, average='weighted')\n",
        "    recall = recall_score(all_labels, all_preds, average='weighted')\n",
        "    f1 = f1_score(all_labels, all_preds, average='weighted')\n",
        "    return precision, recall, f1\n",
        "\n",
        "# Split the dataset\n",
        "train_df, temp_df = train_test_split(token_df, test_size=0.3, random_state=42)\n",
        "val_df, test_df = train_test_split(temp_df, test_size=0.5, random_state=42)\n",
        "\n",
        "# Reset indices after splitting\n",
        "train_df = train_df.reset_index(drop=True)\n",
        "val_df = val_df.reset_index(drop=True)\n",
        "test_df = test_df.reset_index(drop=True)\n",
        "\n",
        "# Token-level datasets\n",
        "max_length = 128\n",
        "train_dataset = TokenDataset(tokenizer, train_df, max_length=max_length)\n",
        "val_dataset = TokenDataset(tokenizer, val_df, max_length=max_length)\n",
        "test_dataset = TokenDataset(tokenizer, test_df, max_length=max_length)\n",
        "\n",
        "# DataLoaders for training, validation, and testing\n",
        "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, collate_fn=custom_collate_fn)\n",
        "val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False, collate_fn=custom_collate_fn)\n",
        "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False, collate_fn=custom_collate_fn)\n",
        "\n",
        "# BERT model\n",
        "model = BertForTokenClassification.from_pretrained('bert-base-uncased', num_labels=2)\n",
        "\n",
        "# Define criterion and optimizer\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "criterion = nn.CrossEntropyLoss(weight=weights.to(device), ignore_index=-100)  # Use class weights\n",
        "optimizer = AdamW(model.parameters(), lr=2e-5)\n",
        "\n",
        "# Learning rate scheduler\n",
        "total_steps = len(train_loader) * 3  # Assuming 3 epochs\n",
        "scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=0, num_training_steps=total_steps)\n",
        "\n",
        "# Train the model\n",
        "train_model(model, train_loader, val_loader, criterion, optimizer, scheduler, epochs=3)\n",
        "\n",
        "# Evaluate on the test set\n",
        "test_precision, test_recall, test_f1 = evaluate_model(model, test_loader, criterion)\n",
        "print(f\"Test Precision: {test_precision}, Test Recall: {test_recall}, Test F1 Score: {test_f1}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bRwHZJd7v1hd",
        "outputId": "9519adf6-3bf3-4f2d-a0ea-c4fd9e1bfa7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n",
            "Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:591: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n",
            "Training Epoch 1/3: 100%|██████████| 2197/2197 [05:49<00:00,  6.30it/s]\n",
            "Evaluating: 100%|██████████| 471/471 [00:25<00:00, 18.12it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3, Loss: 0.5367947634941999, Validation Precision: 0.9727448235130103, Validation Recall: 0.9419144981412639, Validation F1 Score: 0.9557716960791611\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training Epoch 2/3: 100%|██████████| 2197/2197 [05:47<00:00,  6.33it/s]\n",
            "Evaluating: 100%|██████████| 471/471 [00:25<00:00, 18.41it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 2/3, Loss: 0.43961690814252546, Validation Precision: 0.9729489897182212, Validation Recall: 0.9348114710568242, Validation F1 Score: 0.9518538070731488\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training Epoch 3/3: 100%|██████████| 2197/2197 [05:47<00:00,  6.32it/s]\n",
            "Evaluating: 100%|██████████| 471/471 [00:25<00:00, 18.33it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 3/3, Loss: 0.3883595330268484, Validation Precision: 0.9739596896893415, Validation Recall: 0.9352761550716941, Validation F1 Score: 0.9523794860962422\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evaluating: 100%|██████████| 471/471 [00:25<00:00, 18.32it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Precision: 0.9707470729741223, Test Recall: 0.9325589113840027, Test F1 Score: 0.9495883072337215\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Store the model"
      ],
      "metadata": {
        "id": "q5QA_yd9g63m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# After training is complete, save the model\n",
        "model.save_pretrained('/content/gdrive/MyDrive/TimePressure/tp_lrs_model/')\n",
        "\n",
        "# Save the tokenizer\n",
        "tokenizer.save_pretrained('/content/gdrive/MyDrive/TimePressure/tp_lrs_model/')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qGZD77OygxMW",
        "outputId": "09a2a5b3-52b4-434d-e556-90adfb6828e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('/content/gdrive/MyDrive/TimePressure/tp_lrs_model/tokenizer_config.json',\n",
              " '/content/gdrive/MyDrive/TimePressure/tp_lrs_model/special_tokens_map.json',\n",
              " '/content/gdrive/MyDrive/TimePressure/tp_lrs_model/vocab.txt',\n",
              " '/content/gdrive/MyDrive/TimePressure/tp_lrs_model/added_tokens.json')"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 10.1 Predicting out of sample invocations"
      ],
      "metadata": {
        "id": "mvYfMt22wM-K"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 10.2.1 Prediction in President and Home Affairs speeches"
      ],
      "metadata": {
        "id": "CHgSzLFIidMw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import torch\n",
        "from transformers import BertTokenizer, BertForTokenClassification\n",
        "from tqdm import tqdm\n",
        "\n",
        "# Define paths\n",
        "model_path = '/content/gdrive/MyDrive/TimePressure/tp_lrs_model/'\n",
        "prediction_data_path = '/content/gdrive/MyDrive/TimePressure/migration_sample_combined.feather'\n",
        "\n",
        "# Load the trained model\n",
        "model = BertForTokenClassification.from_pretrained(model_path)\n",
        "model.eval()\n",
        "\n",
        "# Load the tokenizer\n",
        "tokenizer = BertTokenizer.from_pretrained(model_path)\n",
        "\n",
        "# Load the new data\n",
        "prediction_df = pd.read_feather(prediction_data_path)\n",
        "\n",
        "# Define a function to prepare the new data\n",
        "def prepare_data(sentences, tokenizer, max_length):\n",
        "    input_ids = []\n",
        "    attention_masks = []\n",
        "\n",
        "    for sentence in sentences:\n",
        "        tokens = tokenizer.tokenize(sentence)\n",
        "        tokens = ['[CLS]'] + tokens + ['[SEP]']\n",
        "\n",
        "        ids = tokenizer.convert_tokens_to_ids(tokens)\n",
        "        ids = ids[:max_length] + [0] * (max_length - len(ids))\n",
        "\n",
        "        mask = [1] * len(tokens)\n",
        "        mask = mask[:max_length] + [0] * (max_length - len(mask))\n",
        "\n",
        "        input_ids.append(ids)\n",
        "        attention_masks.append(mask)\n",
        "\n",
        "    return torch.tensor(input_ids), torch.tensor(attention_masks)\n",
        "\n",
        "# Prepare for batch processing\n",
        "batch_size = 32\n",
        "max_length = 128\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "model.to(device)\n",
        "\n",
        "predicted_labels = []\n",
        "\n",
        "# Create a progress bar\n",
        "with tqdm(total=len(prediction_df), desc=\"Predicting\", unit=\"sentence\") as pbar:\n",
        "    for i in range(0, len(prediction_df), batch_size):\n",
        "        batch_sentences = prediction_df['sentence'][i:i + batch_size]\n",
        "        input_ids, attention_masks = prepare_data(batch_sentences, tokenizer, max_length)\n",
        "\n",
        "        input_ids = input_ids.to(device)\n",
        "        attention_masks = attention_masks.to(device)\n",
        "\n",
        "        with torch.no_grad():\n",
        "            outputs = model(input_ids=input_ids, attention_mask=attention_masks)\n",
        "            logits = outputs.logits\n",
        "\n",
        "        # Convert logits to probabilities\n",
        "        probs = torch.softmax(logits, dim=-1)\n",
        "\n",
        "        # Convert probabilities to predicted labels\n",
        "        batch_predicted_labels = torch.argmax(probs, dim=-1).cpu().numpy()\n",
        "\n",
        "        predicted_labels.extend(batch_predicted_labels.tolist())\n",
        "\n",
        "        # Update progress bar\n",
        "        pbar.update(len(batch_sentences))\n",
        "\n",
        "# Add the predicted label classes to the DataFrame\n",
        "prediction_df['tp_l_token_pred'] = predicted_labels\n",
        "\n",
        "# Save the updated DataFrame\n",
        "updated_prediction_data_path = '/content/gdrive/MyDrive/TimePressure/migration_sample_with_predictions_f.feather'\n",
        "prediction_df.to_feather(updated_prediction_data_path)\n",
        "\n",
        "# Display the updated DataFrame\n",
        "print(prediction_df.head())\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "959371d1-f9a4-47ba-e40d-272dc84f14fd",
        "id": "vbxAsjVCwlfS"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Predicting: 100%|██████████| 73700/73700 [02:31<00:00, 487.52sentence/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   speech_id  sentence_id       date  \\\n",
            "0        602          156 1990-01-17   \n",
            "1        602          151 1990-01-17   \n",
            "2        602           12 1990-01-17   \n",
            "3        602          159 1990-01-17   \n",
            "4        602          157 1990-01-17   \n",
            "\n",
            "                                               title         speaker  \\\n",
            "0  ADDRESS BY PRESIDENT DELORS TO THE EUROPEAN PA...  jacques delors   \n",
            "1  ADDRESS BY PRESIDENT DELORS TO THE EUROPEAN PA...  jacques delors   \n",
            "2  ADDRESS BY PRESIDENT DELORS TO THE EUROPEAN PA...  jacques delors   \n",
            "3  ADDRESS BY PRESIDENT DELORS TO THE EUROPEAN PA...  jacques delors   \n",
            "4  ADDRESS BY PRESIDENT DELORS TO THE EUROPEAN PA...  jacques delors   \n",
            "\n",
            "   portfolio context                                             pdfurl  \\\n",
            "0  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "1  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "2  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "3  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "4  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "\n",
            "                                                 url text.annotated  \\\n",
            "0  https://ec.europa.eu/commission/presscorner/de...           None   \n",
            "1  https://ec.europa.eu/commission/presscorner/de...           None   \n",
            "2  https://ec.europa.eu/commission/presscorner/de...           None   \n",
            "3  https://ec.europa.eu/commission/presscorner/de...           None   \n",
            "4  https://ec.europa.eu/commission/presscorner/de...           None   \n",
            "\n",
            "   after.sampling  length langdetect  \\\n",
            "0           False   51136         en   \n",
            "1           False   51136         en   \n",
            "2           False   51136         en   \n",
            "3           False   51136         en   \n",
            "4           False   51136         en   \n",
            "\n",
            "                                       sentence_text sentence_translated  \\\n",
            "0  This is not a role they have inherited from hi...                None   \n",
            "1  This means that all threats to the system must...                None   \n",
            "2  It is a striking contrast , when seen against ...                None   \n",
            "3  After undergoing radical changes , COMECON mus...                None   \n",
            "4  But this does not mean that the Community is t...                None   \n",
            "\n",
            "  title_translated                                           sentence  year  \\\n",
            "0             None  This is not a role they have inherited from hi...  1990   \n",
            "1             None  This means that all threats to the system must...  1990   \n",
            "2             None  It is a striking contrast , when seen against ...  1990   \n",
            "3             None  After undergoing radical changes , COMECON mus...  1990   \n",
            "4             None  But this does not mean that the Community is t...  1990   \n",
            "\n",
            "                                     tp_l_token_pred  \n",
            "0  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
            "1  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
            "2  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
            "3  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
            "4  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 10.2.2 Aggregation to sentence level predicitions"
      ],
      "metadata": {
        "id": "xvMstOeTx526"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Assuming prediction_df already has the token-level predictions ('tp_l_token_pred')\n",
        "\n",
        "# Initialize columns for the three aggregation methods\n",
        "prediction_df['tp_l_sentence_pred_method1'] = 0  # Method 1: Three-token rule\n",
        "prediction_df['tp_l_sentence_pred_majority'] = 0  # Method 2: Majority vote\n",
        "prediction_df['tp_l_sentence_pred_first_non_negative'] = 0  # Method 3: First non-negative\n",
        "\n",
        "# Method 1: Three-token rule\n",
        "sentence_level_predictions_method1 = {}\n",
        "for sentence in prediction_df['sentence'].unique():\n",
        "    # Get token-level predictions for the current sentence\n",
        "    token_predictions = prediction_df[prediction_df['sentence'] == sentence]['tp_l_token_pred'].tolist()\n",
        "    # Flatten the token predictions if needed\n",
        "    token_predictions = [item for sublist in token_predictions for item in sublist]\n",
        "\n",
        "    # Apply the three-token rule\n",
        "    num_positive_tokens = sum(pred == 1 for pred in token_predictions)  # Counting positive tokens\n",
        "    sentence_level_predictions_method1[sentence] = 1 if num_positive_tokens >= 3 else 0\n",
        "\n",
        "# Map method 1 predictions back to the DataFrame\n",
        "prediction_df['tp_l_sentence_pred_method1'] = prediction_df['sentence'].map(sentence_level_predictions_method1)\n",
        "\n",
        "# Method 2: Majority vote\n",
        "sentence_predictions_majority = {}\n",
        "for sentence in prediction_df['sentence'].unique():\n",
        "    token_predictions = prediction_df[prediction_df['sentence'] == sentence]['tp_l_token_pred'].tolist()\n",
        "    token_predictions = [item for sublist in token_predictions for item in sublist]\n",
        "\n",
        "    # Apply majority voting\n",
        "    if token_predictions:\n",
        "        sentence_predictions_majority[sentence] = max(set(token_predictions), key=token_predictions.count)\n",
        "    else:\n",
        "        sentence_predictions_majority[sentence] = -100\n",
        "\n",
        "# Map method 2 predictions back to the DataFrame\n",
        "prediction_df['tp_l_sentence_pred_majority'] = prediction_df['sentence'].map(sentence_predictions_majority)\n",
        "\n",
        "# Method 3: First non-negative label\n",
        "sentence_predictions_first_non_negative = {}\n",
        "for sentence in prediction_df['sentence'].unique():\n",
        "    token_predictions = prediction_df[prediction_df['sentence'] == sentence]['tp_l_token_pred'].tolist()\n",
        "    token_predictions = [item for sublist in token_predictions for item in sublist]\n",
        "\n",
        "    # Apply first non-negative label rule\n",
        "    sentence_predictions_first_non_negative[sentence] = next((pred for pred in token_predictions if pred != -100), -100)\n",
        "\n",
        "# Map method 3 predictions back to the DataFrame\n",
        "prediction_df['tp_l_sentence_pred_first_non_negative'] = prediction_df['sentence'].map(sentence_predictions_first_non_negative)\n",
        "\n",
        "# Calculate prevalence of positive labels for each method\n",
        "method1_positive_count = (prediction_df['tp_l_sentence_pred_method1'] == 1).sum()\n",
        "method2_positive_count = (prediction_df['tp_l_sentence_pred_majority'] == 1).sum()\n",
        "method3_positive_count = (prediction_df['tp_l_sentence_pred_first_non_negative'] == 1).sum()\n",
        "total_sentences = prediction_df['sentence'].nunique()\n",
        "\n",
        "method1_prevalence = method1_positive_count / total_sentences * 100\n",
        "method2_prevalence = method2_positive_count / total_sentences * 100\n",
        "method3_prevalence = method3_positive_count / total_sentences * 100\n",
        "\n",
        "# Display prevalence results\n",
        "print(f\"Method 1 (Three-token rule) Prevalence: {method1_prevalence:.2f}%\")\n",
        "print(f\"Method 2 (Majority vote) Prevalence: {method2_prevalence:.2f}%\")\n",
        "print(f\"Method 3 (First non-negative rule) Prevalence: {method3_prevalence:.2f}%\")\n",
        "\n",
        "# Calculate correlations between aggregation methods\n",
        "correlation_matrix = prediction_df[['tp_l_sentence_pred_method1', 'tp_l_sentence_pred_majority', 'tp_l_sentence_pred_first_non_negative']].corr()\n",
        "\n",
        "# Display correlation results\n",
        "print(\"\\nCorrelation between aggregation methods:\")\n",
        "print(correlation_matrix)\n",
        "\n",
        "# Display the updated DataFrame for verification\n",
        "print(prediction_df[['sentence', 'tp_l_token_pred', 'tp_l_sentence_pred_method1', 'tp_l_sentence_pred_majority', 'tp_l_sentence_pred_first_non_negative']].head())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "651SSM3o5XNc",
        "outputId": "aeb551a6-4115-43ed-c7b5-2ebb833e55f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Method 1 (Three-token rule) Prevalence: 7.32%\n",
            "Method 2 (Majority vote) Prevalence: 1.67%\n",
            "Method 3 (First non-negative rule) Prevalence: 2.88%\n",
            "\n",
            "Correlation between aggregation methods:\n",
            "                                       tp_l_sentence_pred_method1  \\\n",
            "tp_l_sentence_pred_method1                               1.000000   \n",
            "tp_l_sentence_pred_majority                              0.464597   \n",
            "tp_l_sentence_pred_first_non_negative                    0.598135   \n",
            "\n",
            "                                       tp_l_sentence_pred_majority  \\\n",
            "tp_l_sentence_pred_method1                                0.464597   \n",
            "tp_l_sentence_pred_majority                               1.000000   \n",
            "tp_l_sentence_pred_first_non_negative                     0.727377   \n",
            "\n",
            "                                       tp_l_sentence_pred_first_non_negative  \n",
            "tp_l_sentence_pred_method1                                          0.598135  \n",
            "tp_l_sentence_pred_majority                                         0.727377  \n",
            "tp_l_sentence_pred_first_non_negative                               1.000000  \n",
            "                                            sentence  \\\n",
            "0  This is not a role they have inherited from hi...   \n",
            "1  This means that all threats to the system must...   \n",
            "2  It is a striking contrast , when seen against ...   \n",
            "3  After undergoing radical changes , COMECON mus...   \n",
            "4  But this does not mean that the Community is t...   \n",
            "\n",
            "                                     tp_l_token_pred  \\\n",
            "0  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "1  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "2  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "3  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "4  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "\n",
            "   tp_l_sentence_pred_method1  tp_l_sentence_pred_majority  \\\n",
            "0                           0                            0   \n",
            "1                           0                            0   \n",
            "2                           0                            0   \n",
            "3                           0                            0   \n",
            "4                           0                            0   \n",
            "\n",
            "   tp_l_sentence_pred_first_non_negative  \n",
            "0                                      0  \n",
            "1                                      0  \n",
            "2                                      0  \n",
            "3                                      0  \n",
            "4                                      0  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the updated DataFrame with sentence-level predictions\n",
        "output_path = '/content/gdrive/MyDrive/TimePressure/migration_sample_with_sentence_predictions_f.feather'\n",
        "prediction_df.to_feather(output_path)\n",
        "\n",
        "print(f\"DataFrame with predictions saved to: {output_path}\")\n",
        "# Display the updated DataFrame\n",
        "print(prediction_df.head())\n",
        "\n"
      ],
      "metadata": {
        "id": "-2OoroOc5XD5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "145187b9-5d82-42b2-8489-43dcddaffef9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DataFrame with predictions saved to: /content/gdrive/MyDrive/TimePressure/migration_sample_with_sentence_predictions_f.feather\n",
            "   speech_id  sentence_id       date  \\\n",
            "0        602          156 1990-01-17   \n",
            "1        602          151 1990-01-17   \n",
            "2        602           12 1990-01-17   \n",
            "3        602          159 1990-01-17   \n",
            "4        602          157 1990-01-17   \n",
            "\n",
            "                                               title         speaker  \\\n",
            "0  ADDRESS BY PRESIDENT DELORS TO THE EUROPEAN PA...  jacques delors   \n",
            "1  ADDRESS BY PRESIDENT DELORS TO THE EUROPEAN PA...  jacques delors   \n",
            "2  ADDRESS BY PRESIDENT DELORS TO THE EUROPEAN PA...  jacques delors   \n",
            "3  ADDRESS BY PRESIDENT DELORS TO THE EUROPEAN PA...  jacques delors   \n",
            "4  ADDRESS BY PRESIDENT DELORS TO THE EUROPEAN PA...  jacques delors   \n",
            "\n",
            "   portfolio context                                             pdfurl  \\\n",
            "0  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "1  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "2  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "3  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "4  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "\n",
            "                                                 url text.annotated  ...  \\\n",
            "0  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "1  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "2  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "3  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "4  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "\n",
            "   langdetect                                      sentence_text  \\\n",
            "0          en  This is not a role they have inherited from hi...   \n",
            "1          en  This means that all threats to the system must...   \n",
            "2          en  It is a striking contrast , when seen against ...   \n",
            "3          en  After undergoing radical changes , COMECON mus...   \n",
            "4          en  But this does not mean that the Community is t...   \n",
            "\n",
            "  sentence_translated title_translated  \\\n",
            "0                None             None   \n",
            "1                None             None   \n",
            "2                None             None   \n",
            "3                None             None   \n",
            "4                None             None   \n",
            "\n",
            "                                            sentence  year  \\\n",
            "0  This is not a role they have inherited from hi...  1990   \n",
            "1  This means that all threats to the system must...  1990   \n",
            "2  It is a striking contrast , when seen against ...  1990   \n",
            "3  After undergoing radical changes , COMECON mus...  1990   \n",
            "4  But this does not mean that the Community is t...  1990   \n",
            "\n",
            "                                     tp_l_token_pred  \\\n",
            "0  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "1  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "2  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "3  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "4  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "\n",
            "   tp_l_sentence_pred_method1 tp_l_sentence_pred_majority  \\\n",
            "0                           0                           0   \n",
            "1                           0                           0   \n",
            "2                           0                           0   \n",
            "3                           0                           0   \n",
            "4                           0                           0   \n",
            "\n",
            "   tp_l_sentence_pred_first_non_negative  \n",
            "0                                      0  \n",
            "1                                      0  \n",
            "2                                      0  \n",
            "3                                      0  \n",
            "4                                      0  \n",
            "\n",
            "[5 rows x 22 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 11. Special | Draw random sample for post-stratification tp_l"
      ],
      "metadata": {
        "id": "3WLVyxoDrBaI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Load the prediction DataFrame\n",
        "prediction_df = pd.read_feather('/content/gdrive/MyDrive/TimePressure/migration_sample_with_sentence_predictions_f.feather')\n",
        "\n",
        "# Filter the DataFrame based on the new sentence-level predictions 'tp_l_sentence_pred'\n",
        "positive_sentences_tp_l = prediction_df[prediction_df['tp_l_sentence_pred'] == 1]\n",
        "negative_sentences_tp_l = prediction_df[prediction_df['tp_l_sentence_pred'] == 0]\n",
        "\n",
        "# Get the minimum count between 1000 and the actual number of positive/negative sentences\n",
        "n_positive = min(1000, len(positive_sentences_tp_l))\n",
        "n_negative = min(1000, len(negative_sentences_tp_l))\n",
        "\n",
        "# Randomly sample positive and negative sentences\n",
        "sampled_positive_tp_l = positive_sentences_tp_l.sample(n=n_positive, random_state=42)\n",
        "sampled_negative_tp_l = negative_sentences_tp_l.sample(n=n_negative, random_state=42)\n",
        "\n",
        "# Concatenate the two samples into one balanced DataFrame\n",
        "balanced_sample_tp_l_df = pd.concat([sampled_positive_tp_l, sampled_negative_tp_l])\n",
        "\n",
        "# Shuffle the resulting DataFrame to ensure random distribution\n",
        "balanced_sample_tp_l_df = balanced_sample_tp_l_df.sample(frac=1, random_state=42).reset_index(drop=True)\n",
        "\n",
        "# Save the new balanced dataset to a file\n",
        "balanced_sample_tp_l_data_path = '/content/gdrive/MyDrive/TimePressure/balanced_tp_l_sentence_sample.feather'\n",
        "balanced_sample_tp_l_df.to_feather(balanced_sample_tp_l_data_path)\n",
        "\n",
        "# Display the first few rows of the balanced sample\n",
        "print(balanced_sample_tp_l_df.head())\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CMjUGxVorFFM",
        "outputId": "5ce3acb8-eb91-4fea-8730-6f536f7649ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   speech_id  sentence_id       date  \\\n",
            "0      13748           17 2019-09-29   \n",
            "1      13697            5 2015-06-30   \n",
            "2      14731           39 2002-12-06   \n",
            "3       2475           42 2021-10-12   \n",
            "4      13592          125 2017-05-08   \n",
            "\n",
            "                                               title                speaker  \\\n",
            "0  Remarks by Commissioner Avramopoulos at the ev...  dimitris avramopoulos   \n",
            "1  Remarks by Commissioner Avramopoulos after his...  dimitris avramopoulos   \n",
            "2  Romano Prodi   President of the European Commi...           romano prodi   \n",
            "3  Commissioner Johansson's speech at the Joint S...         ylva johansson   \n",
            "4  Rede von Präsident Juncker anlässlich der Disk...    jean claude juncker   \n",
            "\n",
            "                                 portfolio   context  \\\n",
            "0  Migration, Home Affairs and Citizenship  Brussels   \n",
            "1  Migration, Home Affairs and Citizenship        NA   \n",
            "2                                President        NA   \n",
            "3                             Home Affairs      None   \n",
            "4                                President        NA   \n",
            "\n",
            "                                              pdfurl  \\\n",
            "0  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "1  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "2  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "3  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "4  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "\n",
            "                                                 url text.annotated  \\\n",
            "0  https://ec.europa.eu/commission/presscorner/de...           None   \n",
            "1  https://ec.europa.eu/commission/presscorner/de...           None   \n",
            "2  https://ec.europa.eu/commission/presscorner/de...           None   \n",
            "3  https://ec.europa.eu/commission/presscorner/de...           None   \n",
            "4  https://ec.europa.eu/commission/presscorner/de...           None   \n",
            "\n",
            "   after.sampling  length langdetect  \\\n",
            "0            True   10485         en   \n",
            "1           False    2849         en   \n",
            "2           False   17286         en   \n",
            "3           False   11479         en   \n",
            "4           False   21550         de   \n",
            "\n",
            "                                       sentence_text  \\\n",
            "0  I know well the difficulties you are facing in...   \n",
            "1  Managing these migratory flows can not happen ...   \n",
            "2  The initial decision gave these countries hope...   \n",
            "3  And then forces her into prostitution , abuses...   \n",
            "4  Die Arbeitslosenstände haben sich nach unten k...   \n",
            "\n",
            "                                 sentence_translated  \\\n",
            "0                                               None   \n",
            "1                                               None   \n",
            "2                                               None   \n",
            "3                                               None   \n",
            "4  Unemployment rates have adjusted downwards, wi...   \n",
            "\n",
            "                                    title_translated  \\\n",
            "0                                               None   \n",
            "1                                               None   \n",
            "2                                               None   \n",
            "3                                               None   \n",
            "4  Speech by President Juncker at the discussion ...   \n",
            "\n",
            "                                            sentence  year  \\\n",
            "0  I know well the difficulties you are facing in...  2019   \n",
            "1  Managing these migratory flows can not happen ...  2015   \n",
            "2  The initial decision gave these countries hope...  2002   \n",
            "3  And then forces her into prostitution , abuses...  2021   \n",
            "4  Unemployment rates have adjusted downwards, wi...  2017   \n",
            "\n",
            "                                     tp_l_token_pred  tp_l_sentence_pred  \n",
            "0  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...                   0  \n",
            "1  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...                   0  \n",
            "2  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...                   0  \n",
            "3  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...                   0  \n",
            "4  [1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, ...                   1  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the balanced DataFrame as an Excel file\n",
        "excel_output_path = '/content/gdrive/MyDrive/TimePressure/balanced_tp_l_sentence_sample.xlsx'\n",
        "\n",
        "# Convert to Excel and save\n",
        "balanced_sample_tp_l_df.to_excel(excel_output_path, index=False)\n",
        "\n",
        "# Confirm the save location\n",
        "print(f\"The balanced dataset has been saved to: {excel_output_path}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "As5laRiFrzMc",
        "outputId": "e2eb06b8-37c2-42a5-8929-296605090353"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The balanced dataset has been saved to: /content/gdrive/MyDrive/TimePressure/balanced_tp_l_sentence_sample.xlsx\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 10.2.3 Visualize predicitions"
      ],
      "metadata": {
        "id": "oQ1FEFF9xwZe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Load the saved DataFrame\n",
        "output_path = '/content/gdrive/MyDrive/TimePressure/migration_sample_with_sentence_predictions_f.feather'\n",
        "prediction_df = pd.read_feather(output_path)\n",
        "\n",
        "# Display all column names\n",
        "print(\"Columns in the saved DataFrame:\")\n",
        "print(prediction_df.columns.tolist())\n",
        "\n",
        "# Optionally, display the first few rows to see sample data\n",
        "print(\"\\nSample data from the saved DataFrame:\")\n",
        "print(prediction_df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZeDac2C-VyC2",
        "outputId": "b42ffb78-9f7c-4723-d77f-468b0fe821c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Columns in the saved DataFrame:\n",
            "['speech_id', 'sentence_id', 'date', 'title', 'speaker', 'portfolio', 'context', 'pdfurl', 'url', 'text.annotated', 'after.sampling', 'length', 'langdetect', 'sentence_text', 'sentence_translated', 'title_translated', 'sentence', 'year', 'tp_l_token_pred', 'tp_l_sentence_pred_method1', 'tp_l_sentence_pred_majority', 'tp_l_sentence_pred_first_non_negative']\n",
            "\n",
            "Sample data from the saved DataFrame:\n",
            "   speech_id  sentence_id       date  \\\n",
            "0        602          156 1990-01-17   \n",
            "1        602          151 1990-01-17   \n",
            "2        602           12 1990-01-17   \n",
            "3        602          159 1990-01-17   \n",
            "4        602          157 1990-01-17   \n",
            "\n",
            "                                               title         speaker  \\\n",
            "0  ADDRESS BY PRESIDENT DELORS TO THE EUROPEAN PA...  jacques delors   \n",
            "1  ADDRESS BY PRESIDENT DELORS TO THE EUROPEAN PA...  jacques delors   \n",
            "2  ADDRESS BY PRESIDENT DELORS TO THE EUROPEAN PA...  jacques delors   \n",
            "3  ADDRESS BY PRESIDENT DELORS TO THE EUROPEAN PA...  jacques delors   \n",
            "4  ADDRESS BY PRESIDENT DELORS TO THE EUROPEAN PA...  jacques delors   \n",
            "\n",
            "   portfolio context                                             pdfurl  \\\n",
            "0  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "1  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "2  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "3  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "4  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "\n",
            "                                                 url text.annotated  ...  \\\n",
            "0  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "1  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "2  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "3  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "4  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "\n",
            "   langdetect                                      sentence_text  \\\n",
            "0          en  This is not a role they have inherited from hi...   \n",
            "1          en  This means that all threats to the system must...   \n",
            "2          en  It is a striking contrast , when seen against ...   \n",
            "3          en  After undergoing radical changes , COMECON mus...   \n",
            "4          en  But this does not mean that the Community is t...   \n",
            "\n",
            "  sentence_translated title_translated  \\\n",
            "0                None             None   \n",
            "1                None             None   \n",
            "2                None             None   \n",
            "3                None             None   \n",
            "4                None             None   \n",
            "\n",
            "                                            sentence  year  \\\n",
            "0  This is not a role they have inherited from hi...  1990   \n",
            "1  This means that all threats to the system must...  1990   \n",
            "2  It is a striking contrast , when seen against ...  1990   \n",
            "3  After undergoing radical changes , COMECON mus...  1990   \n",
            "4  But this does not mean that the Community is t...  1990   \n",
            "\n",
            "                                     tp_l_token_pred  \\\n",
            "0  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "1  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "2  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "3  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "4  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "\n",
            "   tp_l_sentence_pred_method1 tp_l_sentence_pred_majority  \\\n",
            "0                           0                           0   \n",
            "1                           0                           0   \n",
            "2                           0                           0   \n",
            "3                           0                           0   \n",
            "4                           0                           0   \n",
            "\n",
            "   tp_l_sentence_pred_first_non_negative  \n",
            "0                                      0  \n",
            "1                                      0  \n",
            "2                                      0  \n",
            "3                                      0  \n",
            "4                                      0  \n",
            "\n",
            "[5 rows x 22 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "# Assuming prediction_df is already loaded and contains 'date', 'tp_h_sentence_pred', and 'portfolio' columns\n",
        "\n",
        "# Ensure the date column is in datetime format\n",
        "prediction_df['date'] = pd.to_datetime(prediction_df['date'])\n",
        "\n",
        "# Extract the year from the date column\n",
        "prediction_df['year'] = prediction_df['date'].dt.year\n",
        "\n",
        "# Calculate positive sentences per year for 'President' portfolio\n",
        "president_df = prediction_df[prediction_df['portfolio'] == 'President']\n",
        "positive_sentences_president = president_df.groupby('year')['tp_l_sentence_pred_first_non_negative'].sum()\n",
        "unique_speech_ids_president = president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Calculate positive sentences per year for non-'President' portfolios\n",
        "non_president_df = prediction_df[prediction_df['portfolio'] != 'President']\n",
        "positive_sentences_non_president = non_president_df.groupby('year')['tp_l_sentence_pred_first_non_negative'].sum()\n",
        "unique_speech_ids_non_president = non_president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Calculate combined positive sentences per year\n",
        "combined_sentences_per_year = positive_sentences_president + positive_sentences_non_president\n",
        "\n",
        "# Plot the results\n",
        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "# Plot line for 'President' portfolio\n",
        "ax1.plot(positive_sentences_president.index, positive_sentences_president.values, marker='o', color='grey', linestyle='-', label='President')\n",
        "ax1.set_ylabel('Number of Implicit Ease of Time Pressure Invocations', color='black')\n",
        "ax1.tick_params(axis='y', labelcolor='blue')\n",
        "\n",
        "# Plot line for non-'President' portfolios\n",
        "ax1.plot(positive_sentences_non_president.index, positive_sentences_non_president.values, marker='o', color='grey', linestyle='--', label='Commissioner')\n",
        "ax1.tick_params(axis='y', labelcolor='black')\n",
        "\n",
        "# Plot combined line\n",
        "ax1.plot(combined_sentences_per_year.index, combined_sentences_per_year.values, marker='o', color='black', linestyle='-', label='Combined')\n",
        "# Set y-axis limits for ax1\n",
        "ax1.set_ylim(0, 400)\n",
        "\n",
        "# Bar plot for unique speech_id for both 'President' and non-'President' portfolios\n",
        "ax2 = ax1.twinx()\n",
        "ax2.bar(unique_speech_ids_president.index, unique_speech_ids_president.values, width=0.4, align='center', color='lightblue', alpha=0.5, label='Speeches President')\n",
        "ax2.bar(unique_speech_ids_non_president.index + 0.4, unique_speech_ids_non_president.values, width=0.4, align='center', color='lightgrey', alpha=0.5, label='Speeches Commissioner')\n",
        "ax2.set_ylabel('Number of speeches', color='gray')\n",
        "ax2.tick_params(axis='y', labelcolor='gray')\n",
        "\n",
        "# Add vertical lines with Commissioner tenure labels and move them further below\n",
        "events = {\n",
        "    '1995': 'Gradin',\n",
        "    '1999': 'Vitorino',\n",
        "    '2004': 'Fratini',\n",
        "    '2008': 'Barrot',\n",
        "    '2010': 'Malmström',\n",
        "    '2014': 'Avramopoulos',\n",
        "    '2019': 'Johansson'\n",
        "}\n",
        "\n",
        "# Plot vertical lines for each event and move the labels lower to avoid overlap with the legend\n",
        "for year, name in events.items():\n",
        "    ax1.axvline(x=int(year), color='darkgrey', linestyle='--', lw=1)\n",
        "    ax1.text(int(year), ax1.get_ylim()[0] + 200, name, color='darkgrey', rotation=90, verticalalignment='bottom', horizontalalignment='center')\n",
        "\n",
        "# Set x-axis to show every year and rotate labels vertically\n",
        "ax1.set_xticks(range(prediction_df['year'].min(), prediction_df['year'].max() + 1))\n",
        "ax1.set_xticklabels(range(prediction_df['year'].min(), prediction_df['year'].max() + 1), rotation=90)\n",
        "\n",
        "# Add legend\n",
        "lines, labels = ax1.get_legend_handles_labels()\n",
        "lines2, labels2 = ax2.get_legend_handles_labels()\n",
        "ax1.legend(lines + lines2, labels + labels2, loc='upper left')\n",
        "\n",
        "# Adjust layout\n",
        "fig.tight_layout()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "outputId": "95135311-fe17-440c-e0a9-51ffa8570810",
        "id": "DquscvLrxpnp"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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G3cv1ICQkBMHBwSgrK0NLSwusra3h7e2tsxbuwbCyssLJkyfx6quvYuHChWhpaYGHhwdmzJgBsViM9vZ2XL16FTt37kRdXR3c3NywatUqPP30030e7+WXX0ZlZSUee+wx8Pl8LF++HPfee6/andW33noLTk5O2Lx5M4qLi2Fra4vIyEi89tprA4570aJF2LdvH6ZNm4bGxkZs374dy5Ytu9PiIIQQQgghhBigOXPm4MSJEzh06BDThTwqKkptIuaJEyeiq6sLv//+Ozo6OuDt7Y1HHnmEtYkEeUpNF382QqquBuXl5fD09FR7raOjAyUlJfDz84OFhQVLERJjI5PJ0NzcDLFYTLOI/n+6utYaGhqQlJSEuLg4mryOo6gO9YeLZc3FmLmIjXKurq6+7T5sjc/kKrpeiCG6VS5mLAynqZUQQgghhBBCCDEylHQTwgI+nw+RSGRQQwyMlVAoxMSJE3vN2E+4g+pQf7hY1lyMmYuonI0D1SMh7KB+rYSwgM/nD3jpOXJnzMzMei1LR7iF6lB/uFjWXIyZi6icjQPVIyHsoGY2QligUCjQ3t5Os4fqQXt7O3Jzc9He3s52KGSQqA71h4tlzcWYuYjK2ThQPRLCDkq6CWEBJd3609HRgaysLHR0dLAdChkkqkP94WJZczFmLqJyNg5Uj4Swg5JuQgghhBBCCCFERyjpJoQQQgghhBBCdISSbkIIIYQQQgghREco6SaEBTweD2ZmZuDxeGyHYvRMTU3h6ekJU1NTtkMhg0R1qD9cLGsuxsxFVM7GgeqREHZQ0q0nCoUC165dQ1ZWFq5du0YTaPVhw4YNGD169B0fZ8eOHbC1tb3j4+iSQCCASCSCQCBgOxSjJxKJMGHCBIhEIrZDIYNEdag/XCxrLsbMRVTOxoHqkRB20DrdepCbm4vExEQ0Nzcz28RiMWbPno2QkBCdnruqqgqbNm3CH3/8gYqKCjg7O2P06NF46aWXMGPGDJ2eW1OvvPIKnn/++Ts+zgMPPIC5c+dqISLdUSqVUCgU4PP51NqtY3K5HFKpFObm5nSTg6OoDvWHi2XNxZi5iMrZOFA9EsIOaunWsdzcXOzZs0ct4QaA5uZm7NmzB7m5uTo797Vr1xAVFYXjx4/j/fffR1ZWFhITEzFt2jSsWrVKZ+cdLJFIBAcHhzs+jqWlJZydnbUQ0Z3p7Ozs9zW5XI6mpibI5XI9RjQ0NTc34+DBg72uQcIdVIf6w8Wy5mLMXETlbByoHglhByXdg9TZ2dnvQyaTAejuUp6YmHjL4yQmJqp1Ne/vmIPx7LPPgsfjIS0tDYsWLUJQUBBGjBiBNWvW4OzZswCAsrIyxMfHQyQSQSwWY/HixaiurmaOoery/c0338Db2xsikQjPPvss5HI53nvvPbi6usLZ2RmbNm1SOzePx8N//vMf3H333bCyskJISAjOnDmDwsJCTJ06FUKhEBMmTEBRUVGvc6kkJydj7NixEAqFsLW1xcSJE1FaWgoAyMzMxLRp02BtbQ2xWIyoqChcuHABQN/dy7du3YqAgACYmZkhODgY3377ba94t23bhnvvvRdWVlYYNmwYfvvtN7V9rly5gjlz5kAkEsHFxQWPPvoobt68ybw+depUPPfcc3jppZfg6OiIWbNmaVhjhBBCCCGEEGND3csHafPmzf2+NmzYMDz88MMoKyu77Z3E5uZmlJWVwdfXFwDw6aefoq2trdd+69ev1yi++vp6JCYmYtOmTRAKhb1et7W1hUKhYBLulJQUyGQyrFq1Cg888ACSk5OZfYuKinD48GEkJiaiqKgI9913H4qLixEUFISUlBScPn0ay5cvR2xsLMaNG8f83ltvvYWPPvoIH330EV599VU8/PDD8Pf3R0JCAry9vbF8+XI899xzOHz4cK/4ZDIZFixYgBUrVuCHH35AZ2cn0tLSmK7YS5YsQUREBLZu3QqBQICMjIx+JwXZv38/XnzxRXzyySeIjY3FwYMH8fjjj8PT0xPTpk1j9tu4cSPee+89vP/++/j888+xZMkSlJaWwt7eHo2NjZg+fTqefPJJfPzxx2hvb8err76KxYsX4/jx48wxdu7ciZUrVyI1NVWj+iKEEEIIIYQYJ0q6dailpUWr+2misLAQSqUSw4cP73efY8eOISsrCyUlJfDy8gIA7Nq1CyNGjMD58+cxZswYAN0t9t988w2sra0RGhqKadOmIS8vD4cOHQKfz0dwcDDeffddnDhxQi3pfvzxx7F48WIAwKuvvoqYmBi88cYbTAvwiy++iMcff7zP2Jqbm9HU1IS7774bAQEBAKA2/r2srAxr165l3t+wYcP6fZ8ffPABli1bhmeffRYAmJb+Dz74QC3pXrZsGR566CEAwNtvv43PPvsMaWlpmD17Nr744gtERETg7bffZvb/5ptv4OXlhfz8fAQFBTFxvPfee/3GQgghhBBCCBlaKOkepISEhH5f4/O7e+1bW1sP6Fg993vxxRfvLLD/T6lU3naf3NxceHl5MQk3AISGhsLW1ha5ublM0u3r66sWo4uLCwQCAfM+VdtqamrUjh8eHq72OgCEhYWpbevo6EBzczPEYrHa79rb22PZsmWYNWsW4uLiEBsbi8WLF8PNzQ1Ad+L85JNP4ttvv0VsbCzuv/9+Jjnv630+9dRTatsmTpyITz/9tN94hUIhxGIx854yMzNx4sSJPmf7LCoqYpLuqKioPmMghBBCCCGEDE00pnuQzMzM+n2YmHTfy/D29u6VTP6dWCyGt7f3bY+rqWHDhoHH4+Hq1asa/+7f/b3bNo/H63Pb35dB67mPqlt4X9v6Wz5t+/btOHPmDCZMmICffvoJQUFBzFj0DRs2IDs7G/PmzcPx48cRGhqK/fv3D/Id9o737+9JIpFg/vz5yMjIUHsUFBRg8uTJzO/01ZW/LwKBAHZ2djRzqB7Y2tpi0aJFBr+MHOkf1aH+cLGsuRgzF1E5GweqR0LYQUm3DvH5fMyePfuW+8yePVutxVhb7O3tMWvWLGzZsgWtra29Xm9sbERISAjKy8tRXl7ObM/JyUFjYyNCQ0O1HtNgREREICEhAadPn8bIkSOxe/du5rWgoCCsXr0aR48excKFC7F9+/Y+jxESEtJrjHVqaqpG7zEyMhLZ2dnw9fVFYGCg2mOgiXZPPB6PeRDd4vF4EAgEVNYcRnWoP1wsay7GzEVUzsaB6pEQdlDSrWMhISFYvHhxrxZv1Uzhulyne8uWLZDL5Rg7dix++eUXFBQUIDc3F5999hliYmIQGxuLsLAwLFmyBOnp6UhLS8PSpUsxZcoUREdH6yyugSgpKUFCQgLOnDmD0tJSHD16FAUFBQgJCUF7ezuee+45JCcno7S0FKmpqTh//ny/Zbl27Vrs2LEDW7duRUFBAT766CPs27cPr7zyyoDjWbVqFerr6/HQQw/h/PnzKCoqwpEjR/D4448PatkvuVyO5uZmWjJMD1paWnDixAmdzJ1A9IPqUH+4WNZcjJmLqJyNA9UjIeygMd16EBISguDgYJSVlaGlpQXW1tbw9vbWSQt3T/7+/khPT8emTZvw8ssvo7KyEk5OToiKisLWrVvB4/Hw66+/4vnnn8fkyZOZlvnPP/9cp3ENhJWVFa5evYqdO3eirq4Obm5uWLVqFZ5++mnIZDLU1dVh6dKlqK6uhqOjIxYuXIiNGzf2eawFCxbg008/xQcffIAXX3wRfn5+2L59O6ZOnTrgeNzd3ZGamopXX30VM2fOhFQqhY+Pz6B7KiiVSshksgGNvSd3RiaToba2llnKj3AP1aH+cLGsuRgzF1E5GweqR0LYwVPSt35cv34dXl5eKC8vh6enp9prHR0dKCkpgZ+fHywsLFiKkBgbmUzGTCCnmgNgqNPVtdbQ0ICkpCTExcXBzs5Oa8cl+kN1qD9cLGsuxsxFbJRzdXX1bfdRTdRKBoauF2KIbpWLGQvqXk4IIYQQQgghhOgIJd2EEEIIIYQQQoiOUNJNCAv4fD6EQqHOx/WT7vkBoqOjYWVlxXYoZJCoDvWHi2XNxZi5iMrZOFA9EsIOGkxKCAv4fD7Mzc3ZDmNIMDc3h7+/P9thkDtAdag/XCxrLsbMRVTOxoHqkRB2UDMbISxQKBSQSqVQKBRsh2L0pFIpiouLIZVK2Q6FDBLVof5wsay5GDMXUTkbB6pHQthBSTchLFAoFGhtbaWkWw/a2tpw4cIFtLW1sR0KGSSqQ/3hYllzMWYuonI2DlSPhLCDkm5CCCGEEEIIIURHKOkmhBBCCCGEEEJ0hJJuQgghhBBCCCFERyjp1hO5XI7k5GT88MMPSE5OhlwuZzskneLxeDhw4EC/r1+7dg08Hg8ZGRmsx8IGHo8HExMT8Hg8tkMxeiYmJnBycoKJCS3WwFVUh/rDxbLmYsxcROVsHKgeCWEHXXF6sG/fPrz44ou4fv06s83T0xOffvopFi5cqNNzV1VVYdOmTfjjjz9QUVEBZ2dnjB49Gi+99BJmzJih03PfipeXFyorK+Ho6MhaDGwSCAQQi8VshzEkWFtbY9q0aWyHQe4A1aH+cLGsuRgzF3GxnOVyOU6dOoXKykq4ublh0qRJEAgEbIfFKi7WIyHGgJJuHdu3bx/uu+8+KJVKte0VFRW47777sHfvXp0l3teuXcPEiRNha2uL999/H2FhYejq6sKRI0ewatUqXL16VSfnHQiBQABXV1fWzs+2nv8fqLVbt5RKJRQKBfh8PpU1R1Ed6g8Xy5qLMXMR18qZzQYPQ8a1eiTEWFD3cg0plUq0trYO6NHc3IwXXnihV8KtOg4AvPjii2hubh7Q8fo6zq08++yz4PF4SEtLw6JFixAUFIQRI0ZgzZo1OHv2LACgrKwM8fHxEIlEEIvFWLx4Maqrq5ljbNiwAaNHj8Y333wDb29viEQiPPvss5DL5Xjvvffg6uoKZ2dnbNq0qdf5KysrMWfOHFhaWsLf3x979+5lXvt79/Lk5GTweDwcO3YM0dHRsLKywoQJE5CXl6d2zF9//RWRkZGwsLCAv78/Nm7cCJlMxrxeUFCAyZMnw8LCAqGhoUhKStKozPRFLpejoaHB6IcZGILGxkb88ssvaGxsZDsUMkhUh/rDxbLmYsxcxKVyVjV49Ey4gf81eOzbt4+lyNjHpXokxJhQS7eG2traIBKJtHIspVKJ69evw8bGZkD7SyQSCIXCAe1bX1+PxMREbNq0qc/fsbW1hUKhYBLulJQUyGQyrFq1Cg888ACSk5OZfYuKinD48GEkJiaiqKgI9913H4qLixEUFISUlBScPn0ay5cvR2xsLMaNG8f83htvvIF33nkHn376Kb799ls8+OCDyMrKQkhISL9x/+tf/8KHH34IJycnPPPMM1i+fDlSU1MBAKdOncLSpUvx2WefYdKkSSgqKsJTTz0FAFi/fj0UCgUWLlwIFxcXnDt3Dk1NTXjppZcGVF6EEEII4T65XI4XX3yx3wYPHo+Hl156CfHx8UO+qzkhRH+opdtIFRYWQqlUYvjw4f3uc+zYMWRlZWH37t2IiorCuHHjsGvXLqSkpOD8+fPMfgqFAt988w1CQ0Mxf/58TJs2DXl5efjkk08QHByMxx9/HMHBwThx4oTa8e+//348+eSTCAoKwltvvYXo6Gh8/vnnt4x706ZNmDJlCkJDQ/HPf/4Tp0+fRkdHBwBg48aN+Oc//4nHHnsM/v7+iIuLw1tvvYX//Oc/AIA///wTV69exa5duzBq1ChMnjwZb7/99mCLkBBCCCEcc+rUqV4t3D0plUqUl5fj1KlTeoyKEDLUsZp0b926FeHh4RCLxRCLxYiJicHhw4eZ16dOnQoej6f2eOaZZ9SOUVZWhnnz5sHKygrOzs5Yu3atWndjbbOysoJEIhnQ49ChQwM65qFDhwZ0PCsrqwHHOZCu6Lm5ufDy8oKXlxezLTQ0FLa2tsjNzWW2+fr6wtramnnu4uKC0NBQ8Pl8tW01NTVqx4+Jien1vOdx+xIeHs787ObmBgDMcTMzM/Hmm29CJBIxjxUrVqCyshJtbW3M+3F3d+83BkIIIYQYr8rKSq3uRwgh2sBq93JPT0+88847GDZsGJRKJXbu3In4+HhcunQJI0aMAACsWLECb775JvM7PRNPuVyOefPmwdXVFadPn0ZlZSWWLl0KU1NTnbVw8ni8AXfxnjlzJjw9PVFRUdFnEszj8eDp6YmZM2dqvYvTsGHDwOPxtDJZmqmpqdpzHo/X5zaFQqHVc6km+FAdVyKRYOPGjX1OgGJhYXHH5yaEEEIIt6lu2GtrP0II0QZWW7rnz5+PuXPnYtiwYQgKCsKmTZsgEomYSb6A7iTb1dWVefRcZuno0aPIycnBd999h9GjR2POnDl46623sGXLFnR2dvZ7XqlUiubmZubR0tKik/cnEAjw6aefAug9Q7Xq+SeffKKTMUX29vaYNWsWtmzZgtbW1l6vNzY2IiQkBOXl5SgvL2e25+TkoLGxEaGhoXccQ896VD2/1Xju24mMjEReXh4CAwN7Pfh8PvN+et69/nsMhkIgEMDGxobGk+mBWCzG3XffTUu0cRjVof5wsay5GDMXcaWcJ02aBE9Pz35n5ubxePDy8sKkSZP0HJlh4Eo9EmJsDGZMt1wux48//ojW1la1LsHff/89HB0dMXLkSCQkJKCtrY157cyZMwgLC4OLiwuzbdasWWhubkZ2dna/59q8eTNsbGyYhzYSzP4sXLgQe/fuhYeHh9p2T09PnS4XBgBbtmyBXC7H2LFj8csvv6CgoAC5ubn47LPPEBMTg9jYWISFhWHJkiVIT09HWloali5diilTpiA6OvqOz//zzz/jm2++QX5+PtavX4+0tDQ899xzgz7eunXrsGvXLmzcuBHZ2dnIzc3Fjz/+iNdffx0AEBsbi6CgIDz22GPIzMzEqVOn8K9//euO34cu8Hg8CAQCWq5DDwQCAaysrOgGB4dRHeoPF8uaizFzEVfKuWeDx9/pusGDC7hSj4QYG9aT7qysLIhEIpibm+OZZ57B/v37mST44YcfxnfffYcTJ04gISEB3377LR555BHmd6uqqtQSbgDM86qqqn7PmZCQgKamJuaRk5Ojg3f2PwsXLsS1a9dw4sQJ7N69GydOnEBJSYnO14n09/dHeno6pk2bhpdffhkjR45EXFwcjh07hq1bt4LH4+HXX3+FnZ0dJk+ejNjYWPj7++Onn37Syvk3btyIH3/8EeHh4di1axd++OGHO7rBMWvWLBw8eBBHjx7FmDFjMH78eHz88cfw8fEBAPD5fOzfvx/t7e0YO3YsnnzyyT6XMjMEcrkcEomElgzTA4lEgtOnT0MikbAdChkkqkP94WJZczFmLuJSOasaPMzNzdW266PBw9BxqR4JMSY8paaLP2tZZ2cnysrK0NTUhL1792Lbtm1ISUnpMzk7fvw4ZsyYgcLCQgQEBOCpp55CaWkpjhw5wuzT1tYGoVCIQ4cOYc6cOQOK4fr16/Dy8kJ5eTk8PT3VXuvo6EBJSQn8/Pxo3DDRGplMhubmZojFYpiY0Mp9gO6utYaGBiQlJSEuLg52dnZaOy7RH6pD/eFiWXMxZi5io5yrq6tvu8/fG196GjFiBNOw8tRTT+HLL78c8i28dL0QQ3SrXMxYsN7SbWZmhsDAQERFRWHz5s0YNWpUv92CVGtAFxYWAgBcXV17fSCrnru6uuowakIIIYQQYsh69nq0sLAY8gk3IYQ9rCfdf6dQKCCVSvt8LSMjA8D/ZpyMiYlBVlaW2lJVSUlJEIvFOh2nTQghhBBCDJdUKkV9fT3z/MaNGyxGQwgZ6ljt15qQkIA5c+bA29sbLS0t2L17N5KTk3HkyBEUFRVh9+7dmDt3LhwcHHD58mWsXr0akydPZtZynjlzJkJDQ/Hoo4/ivffeQ1VVFV5//XWsWrWq1zgeQgghhBAyNPRskAGAiooKliIhhBCWk+6amhosXboUlZWVsLGxQXh4OI4cOYK4uDiUl5fjzz//xCeffILW1lZ4eXlh0aJFzEzVQPcMjAcPHsTKlSsRExMDoVCIxx57TG1db0IMEZ/Ph6WlJfh8g+tsYnQsLCwQFhZGczJwGNWh/nCxrLkYMxdxrZz/PqEuJd3duFaPhBgL1idSMwQ0kRoh7KNrjRBCSE93MpHab7/9hvj4eLi4uKC6uhomJiaQSqV0s5sQA0QTqRFCdEKhUKCzsxMKhYLtUIxeZ2cnKioq0NnZyXYoZJCoDvWHi2XNxZi5iGvlrGrpjoiIAI/Hg0wmw82bN1mOin1cq0dCjAUl3YSwQKFQQCKRUNKtB62trUhNTUVrayvboZBBojrUHy6WNRdj5iKulbMq6fb29oazszMA6mIOcK8eCTEWlHQTQgghhBCjUllZCaB7CVl3d3cAlHQTQthDSTchhBBCCDEqqpZuV1dXeHh4AKCkmxDCHo1nL29vb4dSqYSVlRUAoLS0FPv370doaChmzpyp9QANVc7NFr2eL9TRWq/n05cNGzbgwIEDzBrsxmog73PZsmVobGzEgQMH9BYXIYQQYoxUSbebmxuTdNNa3YQQtmicdMfHx2PhwoV45pln0NjYiHHjxsHU1BQ3b97ERx99hJUrV+oiTqKh2tparFu3Dn/88Qeqq6thZ2eHUaNGYd26dZg4cSLb4bFq2bJl2LlzJwDA1NQU3t7eWLp0KV577TWYmOhmFb1XXnkFzz//vNo2gUCgk3P1NFRuatwKn8+HWCymGWs5jOpQf7hY1lyMmYu4Vs49u5dTS/f/cK0eCenLJ598gqampl7bo6OjMW/ePMhkMhw5cgTZ2dmQyWQIDAzE3LlzIRKJWIi2m8YZRnp6Oj7++GMAwN69e+Hi4oJLly7hl19+wbp16yjpNhCLFi1CZ2cndu7cCX9/f1RXV+PYsWOoq6tjOzSDMHv2bGzfvh1SqRSHDh3CqlWrYGpqioSEhF77dnZ2wszM7I7OJxKJ1C50ExMT2NjY3NExycDY2Nhg9uzZbIdB7gDVof5wsay5GDMXcamclUqlWvdyGtP9P1yqR0L6s2LFCvRc9bqmpgbffvstRowYAQBITExEQUEB7r//fpibm+Pw4cPYs2cPli9fzlbImo/pbmtrg7V1d1fno0ePYuHCheDz+Rg/fjxKS0u1HiDRXGNjI06dOoV3330X06ZNg4+PD8aOHYuEhATcc889zH48Hg9bt27FnDlzYGlpCX9/f+zdu1ftWOXl5Vi8eDFsbW1hb2+P+Ph4XLt2TW2fbdu2ISQkBBYWFhg+fDi+/PJLtdevX7+Ohx56CPb29hAKhYiOjsa5c+fU9vn222/h6+sLGxsbPPjgg2hp+V/3fYVCgc2bN8PPzw+WlpYYNWqUWpwNDQ1YsmQJnJycYGlpiWHDhmH79u23LCNzc3O4urrCx8cHK1euRGxsLH777TcA3S3hCxYswKZNm+Du7o7g4OABlUVycjLGjh0LoVAIW1tbTJw4kbkmNmzYgNGjRzP7yuVyrFmzBra2tnBwcMA//vEPtQ+Pgbzv5ORk8Hg8HDt2DNHR0bCyssKECROQl5cHANixYwc2btyIzMxM8Hg88Hg87Nix45blQgghhHBdU1MTpFIpAGrpJsQYCYVCpkFLJBIhPz8fdnZ28PHxQUdHBy5duoRZs2bBz88P7u7uiI+PR3l5Oa5fv85azBon3YGBgThw4ADKy8tx5MgRZhx3TU0NxGKx1gMkmlP9Bzxw4ADzR6c/b7zxBhYtWoTMzEwsWbIEDz74IHJzcwEAXV1dmDVrFqytrXHq1CmkpqZCJBJh9uzZzPqO33//PdatW4dNmzYhNzcXb7/9Nt544w2m+7ZEIsGUKVNQUVGB3377DZmZmfjHP/6htlRWUVERDhw4gIMHD+LgwYNISUnBO++8w7y+efNm7Nq1C1999RWys7OxevVqPPLII0hJSWHeQ05ODg4fPozc3Fxs3boVjo6OGpWZpaWl2pqVx44dQ15eHpKSknDw4MHbloVMJsOCBQswZcoUXL58GWfOnMFTTz0FHo/X5/nef/99bN++Hf/3f/+Hv/76C/X19di/f7/aPrd73yr/+te/8OGHH+LChQswMTFh7uI98MADePnllzFixAhUVlaisrISDzzwgEblYgwaGhqwb98+NDQ0sB0KGSSqQ/3hYllzMWYu4lI5q1q5bW1tYWFhQWO6e+BSPZKhp6WlBc3NzczjdnkM0N2QdfnyZURERIDH46GyshIKhQL+/v7MPo6OjrCxsUF5ebkuw78ljbuXr1u3Dg8//DBWr16NGTNmICYmBkB3q3dERITWAySaMzExwY4dO7BixQp89dVXiIyMxJQpU/Dggw8iPDxcbd/7778fTz75JADgrbfeQlJSEj7//HN8+eWX+Omnn6BQKLBt2zYmedy+fTtsbW2RnJyMmTNnYv369fjwww+xcOFCAICfnx9ycnLwn//8B4899hh2796N2tpanD9/Hvb29gC6b9z0pFAosGPHDqYHxaOPPopjx45h06ZNkEqlePvtt/Hnn38y/9f8/f3x119/4T//+Q+mTJmCsrIyREREIDo6GgDg6+s74LJSKpU4duwYjhw5ojbmWigUYtu2bUy38u++++6WZREdHY2mpibcfffdCAgIAACEhIT0e97PPvsML730Eu69916YmJjgq6++wpEjR5jXB/K+VTZt2sQ8/+c//4l58+aho6MDlpaWEIlEMDExgaur64DLxBjJZDK2QyB3iOpQf7hY1lyMmYu4Us49x3MDYJLuuro6dHR0wMLCgrXYDAFX6pEMPaGhoWrP169fjw0bNtzyd65evYqOjg6mR6lEIoFAIOh1nQuFQkgkEm2GqxGNk+777rsPd911FyorKzFq1Chm+4wZM3DvvfdqNTgyeIsWLcK8efNw6tQpnD17FocPH8Z7772Hbdu2YdmyZcx+qoSu53PVpFuZmZkoLCxkkmGVjo4OFBUVobW1FUVFRXjiiSewYsUK5nWZTMaMV87IyEBERASTcPfF19dX7Rxubm6oqakBABQWFqKtrQ1xcXFqv9PZ2cnc5Fm5ciUWLVqE9PR0zJw5EwsWLMCECRNuWT4HDx6ESCRCV1cXFAoFHn74YbWLOiwsTG0c9+3KYubMmVi2bBlmzZqFuLg4xMbGYvHixXBzc+t17qamJlRWViIqKorZZmJigujoaKaL+UDet0rPGymq89XU1MDb2/uWZUAIIYQYo57juQHAzs4O5ubmkEqluHHjhloLGCHEcOTk5DA3yYDu4aC3c+nSJQwbNqzXd3RDM6ipml1dXXu1nI0dO1YrARHtsbCwQFxcHOLi4vDGG2/gySefxPr169WS7luRSCSIiorC999/3+s1Jycn5m7R//3f/2HcuHFqr6tm5ra0tLzteUxNTdWe83g8pvu56hx//PGH2kUI/O9CnDNnDkpLS3Ho0CEkJSVhxowZWLVqFT744IN+zzlt2jRs3boVZmZmcHd37zVruVAoVHt+u7IAulu+X3jhBSQmJuKnn37C66+/jqSkJIwfP/62ZfB3A3nfKj3LT9UK37P7PiGEEDKU9FwuDOj+2+jh4YHi4mJUVFRQ0k2IgbK2ttZouHJjYyOKi4uxePFiZptIJIJcLu/Vq6W1tZVbs5e3trbinXfewbFjx1BTU9Pry31xcbHWgiPaFRoa2msN6LNnz2Lp0qVqz1UtqZGRkfjpp5/g7Ozc5wVgY2MDd3d3FBcXY8mSJX2eMzw8HNu2bUN9ff0tW7tvFbO5uTnKysrUulT/nZOTEx577DE89thjmDRpEtauXXvLpFsoFPbq5n4rtysLlYiICERERCAhIQExMTHYvXt3r6TbxsYGbm5uuHjxIjODqEwmw8WLFxEZGanR+74dMzMzyOXyQf8+IYQQwjV/714OQC3pJoQYh4yMDAiFQgQFBTHb3NzcwOfzUVxczHRXv3nzJpqamuDl5cVWqJon3U8++SRSUlLw6KOPws3Nrd+Jogh76urqcP/992P58uUIDw+HtbU1Lly4gPfeew/x8fFq+/7888+Ijo7GXXfdhe+//x5paWn4+uuvAQBLlizB+++/j/j4eLz55pvw9PREaWkp9u3bh3/84x/w9PTExo0b8cILLzBLUEilUly4cAENDQ1Ys2YNHnroIbz99ttYsGABNm/eDDc3N1y6dAnu7u69urb3xdraGq+88gpWr14NhUKBu+66C01NTUhNTYVYLMZjjz2GdevWISoqCiNGjIBUKsXBgwdvOZ56MG5XFl1dXfjvf/+Le+65B+7u7sjLy0NBQYHaDY2eXnjhBbz//vsIDw9HSEgIPvroIzQ2Nmr0vgfC19cXJSUlyMjIgKenJ6ytrQfUVceYWFtbIy4uzuC7HZH+UR3qDxfLmosxcxGXyvnv3csB0GRq/x+X6pGQW1EqlcjIyMCoUaPU1p23sLBAREQEjh49CktLS2bJME9PT3h6erIWr8ZJ9+HDh/HHH39g4sSJuoiHM0IdDffDSiQSYdy4cfj4449RVFSErq4ueHl5YcWKFXjttdfU9t24cSN+/PFHPPvss3Bzc8MPP/zA3BWysrLCyZMn8eqrr2LhwoVoaWmBh4cHZsyYwbT2Pvnkk7CyssL777+PtWvXQigUIiwsDC+99BKA7pbWo0eP4uWXX8bcuXMhk8kQGhqKLVu2DPj9vPXWW3BycsLmzZtRXFwMW1tbREZGMu/FzMwMCQkJuHbtGiwtLTFp0iT8+OOPWijJ/7ldWbS3t+Pq1avYuXMn6urq4ObmhlWrVuHpp5/u83ivvPIKqqqqsGzZMvD5fCxfvhz33nsvmpqaBvy+B2LRokXYt28fpk2bhsbGRmzfvn3AwwuMhYmJCezs7NgOg9wBqkP94WJZczFmLuJSOfeVdNNa3d24VI+E3EpxcTGampr6nMh79uzZOHLkCPbs2QO5XI6AgADMmzePhSj/h6f8++LAt+Hn54dDhw5pvSWRTdevX4eXlxfKy8t73QHp6OhASUkJ/Pz8jG62Sx6Ph/3792PBggVshzLk9Bxrohr/PtTp6lprbW3F1atXMXz48F5j9Qk3UB3qDxfLmosxcxEb5VxdXX3bfVxcXHptCw8PR1ZWFo4ePcpMSPrRRx/h5ZdfxgMPPKD1G/NcQtcLMUS3ysWMhcbrdL/11ltYt24d2tradBEPIUOCUqmEVCqFhve8yCB0dnaiqKhIbR12wi1Uh/rDxbLmYsxcxKVy7m9MN0At3VyqR0KMicbdyz/88EMUFRXBxcUFvr6+vWaeTk9P11pwhBBCCCGEDFRXVxdu3rwJgMZ0E0IMh8ZJN3VFNh7UykoIIYQQY1JTUwOge+yyg4MDs73nmG6lUkkTARNC9ErjpHv9+vW6iIMQQgghhJA7oupa7uLiojajsSrplkqlqK+vV0vICSFE1zROulUuXryI3NxcAMCIESP6nDmOENI3Ho8HCwsLutOuB+bm5ggKChpyS6UZE6pD/eFiWXMxZi7iSjn3NXM50L2MkIODA+rq6lBRUTFkk26u1CMhxkbjpLumpgYPPvggkpOTYWtrCwBobGzEtGnT8OOPP8LJyUnbMRJidAQCAaysrNgOY0iwsrLC6NGj2Q6D3AGqQ/3hYllzMWYu4ko595d0A93juuvq6nDjxg2Eh4frOzSDwJV6JMTYaDx7+fPPP4+WlhZkZ2ejvr4e9fX1uHLlCpqbm/HCCy/oIkZCjI5CoUBXVxcUCgXboRg91aQ6XV1dbIdCBonqUH+4WNZcjJmLuFLOqqTbzc2t12s0gzl36pEQY6Nx0p2YmIgvv/xSbZ3u0NBQbNmyBYcPH9ZqcIQYK4VCgZaWFkq69UAikeD48eOQSCRsh0IGiepQf7hY1lyMmYu4Us59LRem0nMytaGKK/VIiLHROOlWKBS9lgkDAFNTU0ogCCGEEEIIa27XvRwY2kk3IYQdGo/pnj59Ol588UX88MMPancMV69ejRkzZmg9QENVXV2t1/O5uLjo9Xz6smHDBhw4cAAZGRlsh2LQtFVOO3bswEsvvYTGxkatxEUIIYQYkoEk3bRWNyFE3zRu6f7iiy/Q3NwMX19fBAQEICAgAH5+fmhubsbnn3+uixjJINTW1mLlypXw9vaGubk5XF1dMWvWLKSmprIdmkGoqqrC888/D39/f5ibm8PLywvz58/HsWPH2A6tT6+88opWYnvggQeQn5+vhYgIIYQQw6PqXk5jugkhhkTjlm4vLy+kp6fjzz//xNWrVwEAISEhiI2N1XpwZPAWLVqEzs5O7Ny5E/7+/qiursaxY8dQV1fHdmisu3btGiZOnAhbW1u8//77CAsLQ1dXF44cOYJVq1Yx/691ref6obcjEokgEonu+JyWlpawtLS84+Pcqc7OTpiZmenlXDweD+bm5rQ8G4dRHeoPF8uaizFzERfKWalU3rKlm8Z0c6MeCTFGGrd0A90XbFxcHJ5//nk8//zzlHAbmMbGRpw6dQrvvvsupk2bBh8fH4wdOxYJCQm45557mP14PB62bt2KOXPmwNLSEv7+/ti7d6/ascrLy7F48WLY2trC3t4e8fHxuHbtmto+27ZtQ0hICCwsLDB8+HB8+eWXaq9fv34dDz30EOzt7SEUChEdHY1z586p7fPtt9/C19cXNjY2ePDBB9HS0sK8plAosHnzZvj5+cHS0hKjRo1Si7OhoQFLliyBk5MTLC0tMWzYMGzfvr3f8nn22WfB4/GQlpaGRYsWISgoCCNGjMCaNWtw9uxZZr+ysjLEx8dDJBJBLBZj8eLFasMKNmzYgNGjR+Obb76Bt7c3RCIRnn32Wcjlcrz33ntwdXWFs7MzNm3apHZ+Ho+Hr7/+Go888gjEYjFCQkJw5swZFBYWYurUqRAKhZgwYQKKiop6nUslOTkZY8eOhVAohK2tLSZOnIjS0lIAQGZmJqZNmwZra2uIxWJERUXhwoULALq7l6uW+lPZunUrAgICYGZmhuDgYHz77be94t22bRvuvfdeWFlZYdiwYfjtt9/U9rly5QrmzJkDkUgEFxcXPProo7h58ybz+tSpU/Hcc8/hpZdegqOjI2bNmtVv/Wibra0t4uPje71vwh1Uh/rDxbLmYsxcxIVybmlpQXt7O4Bbdy+vqalBZ2enXmMzFFyoR0KM0YCS7s8++wwdHR3Mz7d6EPapWkUPHDgAqVR6y33feOMNLFq0CJmZmViyZAkefPBB5ObmAuheVmLWrFmwtrbGqVOnkJqaCpFIhNmzZzN/rL7//nusW7cOmzZtQm5uLt5++2288cYb2LlzJ4DuWTKnTJmCiooK/Pbbb8jMzMQ//vEPtUn3ioqKcODAARw8eBAHDx5ESkoK3nnnHeb1zZs3Y9euXfjqq6+QnZ2N1atX45FHHkFKSgrzHnJycnD48GHk5uZi69atcHR07PP91tfXIzExEatWrYJQKOz1uuqPkEKhQHx8POrr65GSkoKkpCQUFxfjgQceUNu/qKgIhw8fRmJiIn744Qd8/fXXmDdvHq5fv46UlBS8++67eP3113vdZHjrrbewdOlSZGRkYPjw4Xj44Yfx9NNPIyEhARcuXIBSqcRzzz3X53uQyWRYsGABpkyZgsuXL+PMmTN46qmnmLvWS5YsgaenJ86fP4+LFy/in//8Z5+THwLA/v378eKLL+Lll1/GlStX8PTTT+Pxxx/HiRMn1PbbuHEjFi9ejMuXL2Pu3LlYsmQJ6uvrAXTf5Jk+fToiIiJw4cIFJCYmorq6GosXL1Y7xs6dO2FmZobU1FR89dVXfcZDCCGEDJaqlVssFsPKyqrX646OjszfQ9W+hBCiF8oB8PX1Vd68eZP5ub+Hn5/fQA5ncMrLy5UAlOXl5b1ea29vV+bk5Cjb29vVtldVVen1oam9e/cq7ezslBYWFsoJEyYoExISlJmZmWr7AFA+88wzatvGjRunXLlypVKpVCq//fZbZXBwsFKhUDCvS6VSpaWlpfLIkSNKpVKpDAgIUO7evVvtGG+99ZYyJiZGqVQqlf/5z3+U1tbWyrq6uj7jXL9+vdLKykrZ3NzMbFu7dq1y3LhxSqVSqezo6FBaWVkpT58+rfZ7TzzxhPKhhx5SKpVK5fz585WPP/74gMrl3LlzSgDKffv23XK/o0ePKgUCgbKsrIzZlp2drQSgTEtL6zf2WbNmKX19fZVyuZzZFhwcrNy8eTPzHIDytddeUzY2Niq7urqUZ86cUQJQfv3118w+P/zwg9LCwoJ5vn79euWoUaOUSqVSWVdXpwSgTE5O7jN2a2tr5Y4dO/p8bfv27UobGxvm+YQJE5QrVqxQ2+f+++9Xzp07Vy3e119/nXkukUiUAJSHDx9WKpXd9T1z5ky1Y6iuqby8PKVSqVROmTJFGRER0WdMKv1da3eqsbFR+ccffygbGxu1elyiP1SH+sPFsuZizFzERjlr+v0oOTlZCUAZFBTU7zF9fHyUAHp9rxgq6HohhuhWuZixGNCY7pKSkj5/JoZr0aJFmDdvHk6dOoWzZ8/i8OHDeO+997Bt2zYsW7aM2S8mJkbt92JiYpgZsjMzM1FYWAhra2u1fTo6OlBUVITW1lYUFRXhiSeewIoVK5jXZTIZbGxsAAAZGRmIiIiAvb19v7H6+vqqncPNzQ01NTUAgMLCQrS1tSEuLk7tdzo7OxEREQEAWLlyJRYtWoT09HTMnDkTCxYswIQJE/o8l1Kp7DeOnnJzc+Hl5QUvLy9mW2hoKGxtbZGbm4sxY8b0GbuLiwsEAoHaeG0XFxfm/aiEhYVBLpczr6u29fydjo4ONDc3QywWq/2uvb09li1bhlmzZiEuLg6xsbFYvHgxM2nMmjVr8OSTT+Lbb79FbGws7r//fgQEBPT7Pp966im1bRMnTsSnn36qti08PJz5WSgUQiwWM+8pMzMTJ06c6HPMeVFREYKCggAAUVFRfcagawqFAhKJhJY05DCqQ/3hYllzMWYu4kI532o8t4q7uztKS0uH7LhuLtQjIcZI4zHdb775Jtra2nptb29vx5tvvqmVoIh2WFhYIC4uDm+88QZOnz6NZcuWYf369QP+fYlEgqioKGRkZKg98vPz8fDDD0MikQAA/u///k/t9StXrjBjowcyadffuz7zeDzmj4HqHH/88YfaOXJycphx3XPmzEFpaSlWr16NGzduYMaMGXjllVf6PNewYcPA4/G0NllaX7Hf6v309XuqbuF9bevvj+L27dtx5swZTJgwAT/99BOCgoKYMt+wYQOys7Mxb948HD9+HKGhodi/f/8g32HveP/+niQSCebPn9/r/0lBQQEmT57M/E5f3fkJIYQQbRlI0k0zmBNC2KBx0r1x40YmEeqpra0NGzdu1EpQRDdCQ0PR2tqqtq3nxGGq5yEhIQCAyMhIFBQUwNnZGYGBgWoPGxsbuLi4wN3dHcXFxb1e9/PzA9DdQpqRkcGM/x1MzObm5igrK+t1jp6t0E5OTnjsscfw3Xff4ZNPPsF///vfPo9nb2+PWbNmYcuWLb3KAgCzfnVISAjKy8tRXl7OvJaTk4PGxkaEhoYO6r1oW0REBBISEnD69GmMHDkSu3fvZl4LCgrC6tWrcfToUSxcuLDfieVCQkJ6LSOXmpqq0XuMjIxEdnY2fH19e9URJdqEEEL05VbLhanQWt2EEDZonHQrlco+lxnIzMy8ZRdioj91dXWYPn06vvvuO1y+fBklJSX4+eef8d577yE+Pl5t359//hnffPMN8vPzsX79eqSlpTETeC1ZsgSOjo6Ij4/HqVOnUFJSguTkZLzwwgu4fv06gO6bMJs3b8Znn32G/Px8ZGVlYfv27fjoo48AAA899BBcXV2xYMECpKamori4GL/88gvOnDkzoPdibW2NV155BatXr8bOnTtRVFSE9PR0fP7558xkbevWrcOvv/6KwsJCZGdn4+DBg8yNg75s2bIFcrkcY8eOxS+//IKCggLk5ubis88+Y7rbx8bGIiwsDEuWLEF6ejrS0tKwdOlSTJkyBdHR0ZpViJaVlJQgISEBZ86cQWlpKY4ePYqCggKEhISgvb0dzz33HJKTk1FaWorU1FScP3++3/JYu3YtduzYga1bt6KgoAAfffQR9u3b129Pgb6sWrUK9fX1eOihh3D+/HkUFRXhyJEjePzxx5ku9IQQQoiuDbSle+XKlfDz80N1dXW/D0II0aYBr9NtZ2cHHo8HHo+HoKAgtcRbLpdDIpHgmWee0UmQhkg1DtcQiUQijBs3Dh9//DGKiorQ1dUFLy8vrFixAq+99pravhs3bsSPP/6IZ599Fm5ubvjhhx+YVk4rKyucPHkSr776KhYuXIiWlhZ4eHhgxowZzDjjJ598ElZWVnj//fexdu1aCIVChIWF4aWXXgIAmJmZ4ejRo3j55Zcxd+5cyGQyhIaGYsuWLQN+P2+99RacnJywefNmFBcXw9bWFpGRkcx7MTMzQ0JCAq5duwZLS0tMmjQJP/74Y7/H8/f3R3p6OjZt2oSXX34ZlZWVcHJyQlRUFLZu3Qqgu/v0r7/+iueffx6TJ08Gn8/H7Nmz8fnnnw847lvh8XiwtrbWaK1uFSsrK1y9ehU7d+5EXV0d3NzcsGrVKjz99NOQyWSoq6vD0qVLUV1dDUdHRyxcuLDfXigLFizAp59+ig8++AAvvvgi/Pz8sH37dkydOnXA8bi7uyM1NRWvvvoqZs6cCalUCh8fH8yePXtQ70/bRCIRJk+erJV1zgk7qA71h4tlzcWYuYgL5TzQMd0lJSVobm7WV1gGhQv1SIgx4ikHOLPUzp07oVQqsXz5cnzyySfMRFlAd9Lj6+vba1Iurrh+/Tq8vLxQXl4OT09Ptdc6OjpQUlICPz8/WFhYsBShbvB4POzfvx8LFixgOxRCjPpaI4QQormBtDj3bAQZPXo0MjMzkZiYiFmzZvW5f3JyMvbs2QMHB4d+l+b8+3EJIbp1q1zMWAy4pfuxxx4DAPj5+WHChAn9rvtLCLk9hUIBqVQKc3Nzg2gNNmbt7e0oKipCQEDAgCb2I4aH6lB/uFjWXIyZi7hQzqox3QOZSK2lpUUvMRkaLtQjIcZI42/7U6ZMYRJu1ZJGPR+EkNtTKBRob2+nJTv0oKOjAzk5Oejo6GA7FDJIVIf6w8Wy5mLMXGTo5SyTyVBbWwvg9t3Lge6lR6VSqV5iMySGXo+EGKsBt3SrtLW14R//+Af27NmDurq6Xq/TxEncMdA1qwkhhBBCDFltbS2USiX4fD4cHR373U8oFMLCwoJpOHJyctJjlISQoUrjlu61a9fi+PHj2Lp1K8zNzbFt2zZs3LgR7u7u2LVrly5iJIQQQgghpF+qruUuLi4QCAS33Nfa2hrA0O1iTgjRP41bun///Xfs2rULU6dOxeOPP45JkyYhMDAQPj4++P7777FkyRJdxMk6ahUmRLfoGiOEEDJYA5m5XEUsFqO2tpaSbkKI3micdNfX18Pf3x9A94dWfX09AOCuu+7CypUrtRudATA1NQWPx0NtbS2cnJz6XKOcEE3J5XIolUpIpVLIZDK2w2GdUqlEbW0teDye1idpNDU1hbe3N03+yGFUh/rDxbLmYsxcZOjlrEnSPZRbug29HgkxVhon3f7+/igpKYG3tzeGDx+OPXv2YOzYsfj9999ha2urgxDZJRAI4OnpievXr+PatWtsh0OI0eLxePD09Lxtt0BNiUQijB8/XqvHJPpFdag/XCxrLsbMRYZezqqk283N7bb7qpLuoTgBsKHXIyHGSuOk+/HHH0dmZiamTJmCf/7zn5g/fz6++OILdHV14aOPPtJFjKwTiUQYNmwYurq62A6FGAm5XI6Ojg5YWFhoPcnkKlNTU52UhVwuR1tbG6ysrKisOYrqUH+4WNZcjJmLDL2cB7JcmIpYLAYwNFu6Db0eCTFWGifdq1evZn6OjY3F1atXcfHiRQQGBiI8PFyrwRkSgUBAH05EaxoaGnDs2DHExcXBzs6O7XCMWnNzM5KSkqisOYzqUH+4WNZcjJmLDL2cqXv5wBh6PRJirDROuv/Ox8cHPj4+2oiFEEIIIYQQjWk6kRowNLuXE0LYofGSYS+88AI+++yzXtu/+OILvPTSS9qIiRBCCCGEkAFTdS8fyJhukUgEAJBIJFAoFDqNixBCgEEk3b/88gsmTpzYa/uECROwd+9erQRFCCGEEELIQGnS0i0SicDn86FUKtHa2qrr0AghRPOku66uDjY2Nr22i8Vi3Lx5UytBEUIIIYQQMhASiYRJngeSdPN4PKa1eyiO6yaE6J/GSXdgYCASExN7bT98+DCzfjch5Nbs7OywePFimsRED6isuY/qUH+4WNZcjJmLDLmcVa3cIpGISaZvZ6hOpmbI9UiIMdN4IrU1a9bgueeeQ21tLaZPnw4AOHbsGD788EN88skn2o6PEEIIIYSQfmmyXJjKUF6rmxCifxq3dC9fvhwffvghvv76a0ybNg3Tpk3Dd999h61bt2LFihW6iJEQo9Pc3Ixjx47RH3s9oLLmPqpD/eFiWXMxZi4y5HLWZDy3ylBdq9uQ65EQYzaoJcNWrlyJlStXora2FpaWlgPuykMI6SaXy1FXVwe5XM52KEaPypr7qA71h4tlzcWYuciQy3kwSfdQbek25HokxJhp3NKtUltbi7y8PGRkZAx6ArWtW7ciPDwcYrEYYrEYMTExOHz4MPN6R0cHVq1aBQcHB4hEIixatAjV1dVqxygrK8O8efNgZWUFZ2dnrF27FjKZbLBvixBCCCGEcIgmy4WpDNWWbkIIOzROultbW7F8+XK4ublh8uTJmDx5Mtzc3PDEE0+gra1No2N5enrinXfewcWLF3HhwgVMnz4d8fHxyM7OBgCsXr0av//+O37++WekpKTgxo0bWLhwIfP7crkc8+bNQ2dnJ06fPo2dO3dix44dWLdunaZvixBCCCGEcNCdtHRT0k0I0QeNk+41a9YgJSUFv//+OxobG9HY2Ihff/0VKSkpePnllzU61vz58zF37lwMGzYMQUFB2LRpE0QiEc6ePYumpiZ8/fXX+OijjzB9+nRERUVh+/btOH36NM6ePQsAOHr0KHJycvDdd99h9OjRmDNnDt566y1s2bIFnZ2d/Z5XKpWiubmZedAHLiGEEEIIN1H3ckKIodM46f7ll1/w9ddfY86cOUy38Llz5+L//u//sHfv3kEHIpfL8eOPP6K1tRUxMTG4ePEiurq6EBsby+wzfPhweHt748yZMwCAM2fOICwsDC4uLsw+s2bNQnNzM9Na3pfNmzfDxsaGeYSGhg46bkIGw8rKCuPGjYOVlRXboRg9KmvuozrUHy6WNRdj5iJDLmdV0q1J93JV0i2VSm/ZUGNsDLkeCTFmGifdbW1takmuirOzs8bdywEgKysLIpEI5ubmeOaZZ7B//36EhoaiqqoKZmZmsLW1VdvfxcWF+XCtqqrqFYvquWqfviQkJKCpqYl55OTkaBw3IXfC3NwcPj4+MDc3ZzsUo0dlzX1Uh/rDxbLmYsxcZMjlPJglw8zNzWFmZgZgaHUxN+R6JMSYaZx0x8TEYP369ejo6GC2tbe3Y+PGjYiJidE4gODgYGRkZODcuXNYuXIlHnvsMZ0nwebm5kwrvVgsZu52EqIvHR0dKCgoULuOiG5QWXMf1aH+cLGsuRgzFxlqOcvlctTU1ADQLOkGhuZkaoZaj4QYO42T7k8//RSpqanw9PTEjBkzMGPGDHh5eeH06dP49NNPNQ7AzMwMgYGBiIqKwubNmzFq1Ch8+umncHV1RWdnJxobG9X2r66uZj5UXV1de81mrnqu6QcvIfrU3t6OS5cuob29ne1QjB6VNfdRHeoPF8uaizFzkaGW882bN6FQKMDj8eDk5KTR7w7FydQMtR4JMXYaJ90jR45EQUEBNm/ejNGjR2P06NF45513UFBQgBEjRtxxQAqFAlKpFFFRUTA1NcWxY8eY1/Ly8lBWVsa0qMfExCArK4u5wwkASUlJEIvFNE6bEEIIIcTIqbqWOzs7w8TERKPfpcnUCCH6otmnE7q7pVhZWWHFihV3fPKEhATMmTMH3t7eaGlpwe7du5GcnIwjR47AxsYGTzzxBNasWQN7e3uIxWI8//zziImJwfjx4wEAM2fORGhoKB599FG89957qKqqwuuvv45Vq1bRWBVCCCGEECM3mJnLVYZi93JCCDs0TrqdnZ1x77334pFHHsGMGTPA52vcWM6oqanB0qVLUVlZCRsbG4SHh+PIkSOIi4sDAHz88cfg8/lYtGgRpFIpZs2ahS+//JL5fYFAgIMHD2LlypWIiYmBUCjEY489hjfffHPQMRFCCCGEEG64k6R7KHYvJ4SwQ+Oke+fOndi9ezfi4+NhY2ODBx54AI888giio6M1PvnXX399y9ctLCywZcsWbNmypd99fHx8cOjQIY3PTQibTExM4OLionFXOKI5KmvuozrUHy6WNRdj5iJDLWdtJN1DqXu5odYjIcZO4yvu3nvvxb333ouWlhbs3bsXP/zwA8aPHw9/f3888sgjWLdunS7iJMSoWFtbY8qUKWyHMSRQWXMf1aH+cLGsuRgzFxlqOavGdGuyRrfKUOxebqj1SIixG3TfcGtrazz++OM4evQoLl++DKFQiI0bN2ozNkKMlkKhQFdXFxQKBduhGD0qa+6jOtQfLpY1F2PmIkMtZ211L1cqlVqNy1AZaj0SYuwGnXR3dHRgz549WLBgASIjI1FfX4+1a9dqMzZCjFZTUxP279+PpqYmtkMxelTW3Ed1qD9cLGsuxsxFhlrOd5J0i0Qi8Hg8KBQKtLW1aTs0g2So9UiIsdO4e/mRI0ewe/duHDhwACYmJrjvvvtw9OhRTJ48WRfxEUIIIYQQ0qc76V7O5/MhFAohkUjQ0tICoVCo7fAIITrS3NyMP//8E4WFhejq6oK9vT3i4+Ph7u4OAFAqlUhOTkZ6ejo6Ojrg5eWFefPmwcHBgZV4BzWm++6778auXbswd+5cmJqa6iIuQgghhBBCbulOWrqB7i7mEokEzc3Ngz4GIUS/2tvb8c0338DPzw9LliyBlZUV6uvrYWFhweyTmpqKc+fOYcGCBbCzs8OJEyfw3XffYdWqVaxMJKjxGaurq5kxMIQQQgghhLChq6uLmQRtsAmzWCxGZWXlkJpMjRBD1dLSoraagLm5OczNzXvtl5qaChsbG8THxzPb7OzsmJ+VSiXOnTuHyZMnY/jw4QCABQsW4IMPPsDVq1cxcuRIHb6LvmmcdFtbW0OhUKCwsBA1NTW9JmKgbuaEEEIIIUTXJBIJAMDS0nLQDUK0VjchhiM0NFTt+fr167Fhw4Ze++Xl5SEgIAA///wzrl27BrFYjOjoaERFRQEAGhsbIZFI4O/vz/yOhYUFPD09UV5ezo2k++zZs3j44YdRWlraa6ZHHo8HuVyuteAIMVY2Nja45557YGZmxnYoRo/KmvuoDvWHi2XNxZi5yBDLWZV0u7m5gcfjDeoYQ22tbkOsR0JUcnJy4OHhwTzvq5UbABoaGnDhwgXExMTgrrvuwo0bN5CYmAiBQIDRo0cznw1/n6dBKBSitbVVd2/gFjROup955hlER0fjjz/+uKMPOUKGMj6frzbuhOgOlTX3UR3qDxfLmosxc5EhlrPqi/WdjMUeamt1G2I9EqJibW3NXJO3olQq4e7ujhkzZgDovvFWU1ODixcvYvTo0TqOcnA0XjKsoKAAb7/9NkJCQmBrawsbGxu1ByHk9iQSCf766y/mCwPRHSpr7qM61B8uljUXY+YiQyxnbSTdQ617uSHWIyGasra2hpOTk9o2R0dHZik8kUgEAL1atVtbW1lbpUDjpHvcuHEoLCzURSyEDBldXV24ceMGurq62A7F6FFZcx/Vof5wsay5GDMXGWI59+xePliqVrWh0r3cEOuREE15eXmhrq5ObVtdXR3TAGxrawuRSITi4mLmdalUiuvXr8PLy0uvsapo3L38+eefx8svv4yqqiqEhYX1WjIsPDxca8ERQgghhBDSF220dKtaxNrb2yGTyVhZSogQopnx48fjm2++walTpzBixAhUVFQgPT0dd999N4DuecbGjRuHU6dOwcHBAba2tjhx4gSsra2Z2cz1TeNPlkWLFgEAli9fzmzj8XhQKpU0kRohhBBCCNELbSTdlpaWMDExgUwmQ0tLi9qyQ4QQw+Th4YEHHngAx44dQ0pKCuzs7DBr1iy1xt+JEyeiq6sLv//+Ozo6OuDt7Y1HHnmEtRtrGp+1pKREF3EQQgghhBAyYNpIuoHuLub19fWUdBPCIUFBQQgKCur3dR6Ph2nTpmHatGl6jKp/GifdPj4+uoiDkCHF0tISo0aNgqWlJduhGD0qa+6jOtQfLpY1F2PmIkMsZ9XkZ3cyphvonpRJlXQbO0OsR0KGggEn3b/99tuA9rvnnnsGHQwhQ4WFhQWCg4PZDmNIoLLmPqpD/eFiWXMxZi4ytHJWKpXMzMR32tI9lNbqNrR6JGSoGHDSvWDBgtvuQ2O6CRmYzs5OVFdXw8XFBWZmZmyHY9SorLmP6lB/uFjWXIyZiwytnNvb26FQKMDj8eDs7HxHxxpKa3UbWj0SMlQMeMkwhUJx2wcl3IQMTGtrK86cOdNr/UCifVTW3Ed1qD9cLGsuxsxFhlbOqvHcjo6OvVbS0dRQWqvb0OqRkKFC43W6CSGEEEIIYZO2JlEDht5a3YQQ/aOkmxBCCCGEcIo2k+6h1NJNCGEHJd2EEEIIIYRTKOkmhHAJJd2EsEAgEMDW1hYCgYDtUIwelTX3UR3qDxfLmosxc5GhlbO2lgsD/pd0y2QytLe33/HxDJmh1SMhQ4XG63QTQu6cWCzGzJkz2Q5jSKCy5j6qQ/3hYllzMWYuMrRy1mZLt0AggJWVFdra2tDS0mLUa1gbWj0SMlQMqqW7sbER27ZtQ0JCAurr6wEA6enpqKio0GpwhBBCCCGE/J02k26AJlMjhOiWxkn35cuXERQUhHfffRcffPABGhsbAQD79u1DQkKCtuMjxCg1NDRg7969aGhoYDsUo0dlzX1Uh/rDxbLmYsxcZGjlrEq6tdG9HBg647oNrR4JGSo0TrrXrFmDZcuWoaCgABYWFsz2uXPn4uTJk1oNjhBjplAo2A5hyKCy5j6qQ/3hYllzMWYuMqRy1nZL91BJugHDqkdChgqNk+7z58/j6aef7rXdw8MDVVVVWgmKEEIIIYQYnpqaGta7YMtkMnR0dACg7uWEEG7QOOk2Nzfv8wMpPz8fTk5OWgmKEEIIIYSw6/Tp0ygoKAAAyOVyAN3DDI8cOYLr16+zFpeqldvExAQ2NjZaOeZQaukmhOifxkn3PffcgzfffBNdXV0AAB6Ph7KyMrz66qtYtGiR1gMkhBBCCCH6d/PmTaZBpaamBgAwbdo0REREICcnh7W4VEm3SCQCj8fTyjFVSTe1dBNCdEHjpPvDDz+ERCKBs7Mz2tvbMWXKFAQGBsLa2hqbNm3SRYyEGB1ra2vMmjWL+SNPdIfKmvuoDvWHi2XNxZi5oqurC2ZmZgC6k1E/Pz/Y2trCzc2NSXzZ0DPp1hZV93Jjb+mm64UQdmi8TreNjQ2SkpKQmpqKzMxMSCQSREZGIjY2VhfxEWKUtNkljtwalTX3UR3qDxfLmosxc4WlpSXq6upgZmaG6upqjB8/HiYmJpBIJODzB7XqrFboIulWJaGtra1MV3pjRNcLIezQKOnu6uqCpaUlMjIyMHHiREycOFFXcRFi1FpbW5GTk4PQ0FAIhUK2wzFqVNbcR3WoP1wsay7GzBVBQUE4e/YsTExMYGlpidLSUohEItTW1rKauKmSbm221lpZWUEgEEAul7Paiq9rdL0Qwg6NblOamprC29vbqO8AEqIPnZ2dKCkpQWdnJ9uhGD0qa+6jOtQfLpY1F2PmisDAQMyYMQNjxoxBVFQUrl27hs7OTgiFQoSFhbEWly5auoGhMZkaXS+EsEPjvkH/+te/8Nprr6G+vl4X8RBCCCGEEANhb28PT09PCAQCZpu7uzscHR1Zi0nXSTdNpkYI0TaNx3R/8cUXKCwshLu7O3x8fHp1TUlPT9dacIQQQgghhD3Xrl1DXl4e0/p75swZhIaGwtfXl7WYdJV0D5XJ1Agh+qdx0r1gwQIdhEEIIYQQQgxJXl4erly5gsDAQPj6+iIzMxMODg64ePEipFIpgoODWYlLlRRT93JCCFdonHSvX79eF3EQMqSYm5tj+PDhMDc3ZzsUo0dlzX1Uh/rDxbLmYsxcUVhYiKioKPj6+qKtrQ1SqRSBgYFwcXFBdnY2K0m3UqlEa2srAOpePhh0vRDCDo2TbkLInbOyskJ4eDjbYQwJVNbcR3WoP1wsay7GzBXt7e1wcHAAoF7ODg4OaG9vZyWmjo4OZkJfbc++PRS6l9P1Qgg7NJ5Ijc/nQyAQ9PsghNxeV1cXampq0NXVxXYoRo/KmvuoDvWHi2XNxZi5QiQSoby8HIB6OZeXl2t1uS5NqMZzq5b40qah0L2crhdC2KFxS/f+/fvVnnd1deHSpUvYuXMnNm7cqLXACDFmEokEycnJiIuLg52dHdvhGDUqa+6jOtQfLpY1F2PmipEjR+LMmTO4efMmhEIhioqK4ODggPr6esTExLASk64mUQPUu5crlUrweDytn4NtdL0Qwg6Nk+74+Phe2+677z6MGDECP/30E5544gmtBEYIIYQQQtjj6emJGTNmID8/HzU1NQAAU1NTxMbGspaw6SPp7urqQlNTE2xtbbV+DkLI0KRx9/L+jB8/HseOHdPW4QghhBBCCMvs7e0xfvx4jB8/HgAQFhbGagupLpNuU1NTWFhYAAAqKiq0fnxCyNCllaS7vb0dn332GTw8PLRxOEIIIYQQwoKurq4+HzKZDAAgk8mYbWzQ1XJhKqrJ1G7cuKGT4xNChiaNu5fb2dmpjXFRKpVoaWmBlZUVvvvuO60GR4ix4vF4sLS0NMrxYoaGypr7qA71h4tlzcWYDdn+/fv7LEulUgkAOHHiBIDucr///vv1Ghug25ZuoLuLeU1NjdG2dNP1Qgg7NE66P/74Y7ULlc/nw8nJCePGjaMJGQgZIFtbW8yfP5/tMIYEKmvuozrUHy6WNRdjNmRTp05lO4Rb0kfSDRhm9/Lq6urb7uPi4nLL1+l6IYQdGifdy5Yt00EYhBBCCCGEbc7OzmyHcEu6TrpV3csNMekmhHCXxmO6ExMT8ddffzHPt2zZgtGjR+Phhx9GQ0ODVoMjxFg1Njbi999/R2NjI9uhGD0qa+6jOtQfLpY1F2PmitraWuZRXFyMAwcOoLi4mNnGBn21dBvrmG66Xghhh8ZJ99q1a9Hc3AwAyMrKwpo1azB37lyUlJRgzZo1Wg+QEGOkVCrR3t7OjJEjukNlzX1Uh/rDxbLmYsxcceLECeZx4cIFdHZ24sKFC0hOTkZycrLe45HL5WhvbwdALd2DRdcLIezQuHt5SUkJQkNDAQC//PIL5s+fj7fffhvp6emYO3eu1gMkhBBCCCH6t2DBAubnxsZGJCcnIzIyEteuXUNYWJje41G1cgsEAlhaWurkHIY8ppsQwl0aJ91mZmZoa2sDAPz5559YunQpgO51HFUt4IQQQgghuiKVSnHlyhXU1tYyLZ8nTpwAn9/dga9nskgGz8zMjPnZ1NQUAODg4ACxWIzMzEzExcXpNR5ddy0H/pd0V1dXQyaTwcRE46/KhBDSi8afJHfddRfWrFmDiRMnIi0tDT/99BMAID8/H56enloPkBBCCCGkp3PnzkEikcDPzw8KhQLZ2dkIDg6GUChkO7QhwcLCglkvW5/0kXQLhULw+XwoFApUV1fDw8NDZ+cihAwdGifdX3zxBZ599lns3bsXW7duZT6MDh8+jNmzZ2s9QEKMkUgkwtSpU3X6xYF0o7LmPqpD/eFKWd+8eRPTp0+Hra0turq64OTkBDs7O6Y1lmhHz8m25HI5oqOj0draioKCAtja2uo9Hn0k3Twejzl+RUWF0SXdXLnGCTE2Gifd3t7eOHjwYK/tH3/8sVYCImQoMDU1NfhlWYwFlTX3UR3qD1fK2traGnK5HAB3Yuaio0ePgsfj9Zp0y8HBAWPGjNF7PPpIugHjnkyNrhdC2KFx0p2eng5TU1NmAo1ff/0V27dvR2hoKDZs2KA2/ocQ0re2tjYUFhYiMDAQVlZWbIdj1KisuY/qUH+4UtZRUVG4fPkyQkNDYWZmhpKSEvj7+zMxU4u3dsybN4/5ub29HaWlpQgKCmLGPeubKunW9fmNeTI1rlzjhBgSqVSKkpISODg4wMnJaVDH0HjJsKeffhr5+fkAgOLiYjz44IOwsrLCzz//jH/84x8aHWvz5s0YM2YMrK2t4ezsjAULFiAvL09tn6lTp4LH46k9nnnmGbV9ysrKMG/ePFhZWcHZ2Rlr166FTCbT9K0RojdSqRRXr16FVCplOxSjR2XNfVSH+sOVsjY1NUVXVxdSUlJw9OhRFBQU4MiRI9i/fz8OHDjAdnhGQygUMg+BQICioiJWv1/pq6XbmNfq5so1Tgibfv75Z6SlpQEAurq68N///hc///wzvvrqK+Tk5AzqmBq3dOfn52P06NFMQJMnT8bu3buRmpqKBx98EJ988smAj5WSkoJVq1ZhzJgxkMlkeO211zBz5kzk5OSoTYayYsUKvPnmm8zznnfm5HI55s2bB1dXV5w+fRqVlZVYunQpTE1N8fbbb2v69gghhBBi4M6dOwc+n49x48ahq6sLFy9eRFRUFGstsMaspqYGeXl5zPjuS5cuISwsbNCtPXeCupdrpqOjA5mZmaiurmaSbNVQgaSkJCxevJjN8AgxWKWlpZg0aRIA4OrVqwCAf/7zn8jIyMCpU6eY5bM1oXHSrVQqoVAoAHQvGXb33XcDALy8vHDz5k2NjpWYmKj2fMeOHXB2dsbFixcxefJkZruVlRVcXV37PMbRo0eRk5ODP//8Ey4uLhg9ejTeeustvPrqq/12d5dKpWp3+NiYgZMQQgghg9PU1IS4uDiIxWI0NDQA6F661M7OjuXIjEtpaSnS0tLg6ekJb29v5OXlQSAQICUlBWPGjIGPj49e41F9X9NXSzfXk+60tDS0tbUhNDSUWddcIpEgMzMTo0aNYjk6QgyXVCplrpnCwkKEhITA1NQUQUFBSEpKGtQxNe5eHh0djX//+9/49ttvkZKSwoz3KSkpgYuLy6CCUGlqagLQ/Yezp++//x6Ojo4YOXIkEhISmHXCAeDMmTMICwtTO/esWbPQ3NyM7OzsPs+zefNm2NjYMI/B3K0ghBBCCDvs7OyY9bmJ7uTk5CA8PBwxMTHw9vYGAISHhyMsLGzQXSzvhL67l3M96b558ybGjRuHwMBAeHh4wMPDg5lEjSZTI6R/YrEY169fR2dnJwoLCxEQEACge24LExON26wBDKKl+5NPPsGSJUtw4MAB/Otf/0JgYCAAYO/evZgwYcKgggAAhUKBl156CRMnTsTIkSOZ7Q8//DB8fHzg7u6Oy5cv49VXX0VeXh727dsHAKiqquqV7KueV1VV9XmuhIQErFmzhnleUVFBiTfRKzMzM/j5+dHEg3pAZc19VIf6w5WyHjZsGC5duoTg4GCYm5vDw8MDHR0dTBdoNpazMkatra1wd3cHoP5/w93dHVlZWXqNpaOjg5mxXtfrsRvLmO6+JkrjyjVOCJvGjx+Pffv2wczMDDY2NvD19QXQ3ftnsI3MGifd4eHhfX7Qvv/++xAIBIMKAgBWrVqFK1eu4K+//lLb/tRTTzE/h4WFwc3NDTNmzEBRURFz10FT5ubmMDc3Z543NzcPLmhCBkkoFLKy3MpQRGXNfVSH+sOVsj5z5gwA4Pz588y2GzduQKlUgsfj4f7772crNKNiZWWFmpoaWFtbq/3fKCws1PvM16qu5RYWFoNuaRoo1Zju5uZmSCQSzq5pPXr0aFy+fBnR0dHMjQquXOOEsGnMmDHw8PBAU1MTAgICwOPxAHT3spo2bdqgjjmoT63Gxkbs3bsXRUVFWLt2Lezt7ZGTkwMXFxd4eHhofLznnnsOBw8exMmTJ+Hp6XnLfceNGwcATFO/q6srM7ucSnV1NQD0Ow6cELbJZDK0trZCKBTq/MvDUEdlzX1Uh/rDlbLuuZSVXC5HW1sbrKys7ujmP+ktKCgIly5dQmNjI+zs7CCVSiGRSFBaWoqIiAi9xtLa2gpA98uFAd2twdbW1mhpaUFFRQWCg4N1fk5dOHPmDORyOQ4dOgSBQAA+nw+lUsncnLr33nvZDpEQg+Xu7g4XFxc0NDTA3t4efD4fQUFBgz6exmO6L1++jGHDhuHdd9/FBx98wHTl2rdvHxISEjQ6llKpxHPPPYf9+/fj+PHj8PPzu+3vZGRkAADc3NwAADExMcjKykJNTQ2zT1JSEsRiMXUZJwarpaUFR44coUn89IDKmvuoDvWHK2XdcykruVyOkydPQi6XM9uIdgQGBmL8+PFoampCZmYmsrKy0NDQgJiYmEH3NhwsfY3nVlF1q+fyuO6IiAhER0djzJgxiIyMxOjRoxEUFASZTHZHyQMhxq6rqwu//vorNm3ahC+//JKZd+zQoUO9emUPlMZJ95o1a/D444+joKAAFhYWzPa5c+fi5MmTGh1r1apV+O6777B7925YW1ujqqoKVVVVzOQoRUVFeOutt3Dx4kVcu3YNv/32G5YuXYrJkycjPDwcADBz5kyEhobi0UcfRWZmJo4cOYLXX38dq1atUutCTgghhBDjIZFIkJ6ejosXLwLoXtZFlZgR7fH09MT06dMxdepUAP/rdqlv+k66Ve+Ry+O6fX19ez1UNxNU/xJCevvzzz9RXV2NZcuWqfX68vf373ei7tvROOk+f/48nn766V7bPTw8+p24rD9bt25FU1MTpk6dCjc3N+bx008/Aeju3vPnn39i5syZGD58OF5++WUsWrQIv//+O3MMgUCAgwcPQiAQICYmBo888giWLl2qtq43IYQQQritoqKCWbK0qqoKiYmJqK+vZ5KwpqYmJCYmavxdhNxefX09k3yyNQ+OvpYLU1El3Vxu6Qa6Jyq+fv06cnJykJOTo9YzlBDSt7y8PMydOxfe3t7MeG6ge9b/+vr6QR1T4wFb5ubmfX7g5ufnw8nJSaNjKZXKW77u5eWFlJSU2x7Hx8cHhw4d0ujchBBCCOGO1NRU3HPPPbCwsMDly5cRFBSE8PBwNDQ0oKysDOPGjUN5eTkuX75Mc7poSVtbG86ePYubN28yrT3nzp1DUVERxo8fr9fJ1Nhq6eZy0t3S0oJTp06hvb2dGQuv+g7f1tZG69oT0g/V3CZ/19nZqZaEa0LjpPuee+7Bm2++iT179gAAeDweysrK8Oqrr2LRokWDCoKQoYjP17ijCRkkKmvuozrUH0Mt68WLFzM/Nzc3IyYmhnmuitnPzw/5+fl6j81YXbhwAQqFArNnz4ZcLsexY8cwfvx45OXl4cKFC5g8ebLeYqEx3Zq7dOkSRCIRZsyYwQy5rK6uRkpKCvLy8lgZJkCINiQnJ/dqmHVwcMBzzz0HoHtS0CNHjiA7OxsymQyBgYGYO3fugD8/3N3dkZ+fz0zgrZKenn7bSb/7o3HS/eGHH+K+++6Ds7Mz2tvbMWXKFFRVVSEmJgabNm0aVBCEDDV2dna477772A5jSKCy5j6qQ/0x5LJOTU3F2LFjYWpqCnNzczQ2NsLa2lot5vLycrX5Zsidqa2txfTp05kltFTlbGVlhRMnTug1FhrTrbna2lq1hBsAXFxcMHPmTBw/fpzFyAi5c05OTli6dCnzvOcN48TERBQUFOD++++Hubk5Dh8+jD179mD58uUDOvaMGTPw/fffo7a2FgqFAufOnUNtbS3Ky8uxbNmyQcWrcdJtY2ODpKQkpKamIjMzExKJBJGRkYiNjR1UAIQQQgght2Nqasr87O/vjwsXLkAikcDR0REAcPPmTVy9epVmZdYiS0tLZhx9T0qlEpaWlnqNRZV062PJMMA4upcLBALIZLJe22UymcH2aCFkoPh8fp834To6OnDp0iUsWrSIWRkrPj4eW7ZswfXr1wfUUu3t7Y2nn34af/31F5ydnVFUVAQ3Nzc88cQTcHFxGVS8GiXdXV1dsLS0REZGBiZOnIiJEycO6qSEDHXNzc04e/Ysxo8fz7QgEN2gsuY+qkP9MeSyHjt2LPNzaGgoTExMkJ+fj6ysLADdc86MGDECw4YNYytEozNq1ChcunQJkZGRMDExwdmzZxESEoL8/HyMGjVKb3Go1mIHoLcl4VRJd2VlJRQKBSeTVDc3N1y4cAFjxoyBvb09gO7eIGlpaTTvATFILS0tanOHmZub97saVX19PT788EOYmJjAy8sLM2bMgI2NDXPN+vv7M/s6OjrCxsYG5eXlA+4ebm9vj3vuuefO3lAPGiXdpqam8Pb2hlwu11oAhAxFcrkcjY2NdC3pAZU191Ed6g8XylqhUKCsrAw+Pj4IDg5GbW0tTpw4gcmTJ9PEUFqWlpbGjOUGulu4z5w5Az6fj/Pnz+P8+fPMvgsWLNBZHK2trQC6W7b01cLu4uICHo8HmUyGmpoaTiapERERSEtLw7Fjx5ibBqqeC9QjhBii0NBQtefr16/Hhg0beu3n4eGB+Ph4ODo6oqWlBSkpKdi+fTtWrlwJiUQCgUDQa6iRUCjUaFlJpVKJ+vp6tLa29pr828fHZ+Bv6v/TuHv5v/71L7z22mv49ttvmbtmhBBCCCH6wOfzcfHiRcyePRsA1NZQJdoVERHB/Nza2ors7GyMGDFCb63NKj3Hcw925mBNmZqawsXFBVVVVbhx4wYnk24zMzPcddddaGlpYZZcUyqVSE1NVRuuQYihyMnJUZvgr79W7p49mlxcXODp6YlPPvkE2dnZWvm/ff36dfzyyy9oamrqlXDzeDysW7dO42Nq/Jfqiy++QGFhIdzd3eHj49Prgzc9PV3jIAghRBfa2trQ0dHBdhiEEC2zt7dHY2Oj3pO/ocbX15f5uaGhAdnZ2XB3d9d7jwJ9T6Km4uHhgaqqKlRUVCAyMlKv59Yma2trWFtbM2t2E2KorK2tBzW0ycLCAg4ODqivr0dAQADkcjk6OjrUWrtbW1sH/Bly8OBBuLu74+GHH9baPBIaJ93x8fF6u8tICCGaUiqVyMnJQX5+PmQyGXOHsri4GJGRkfT5RYgRCAwMREZGBtra2piW7paWFub6trW1ZTE646JQKHDjxg1UV1cDAGpqamBjY6PXMc5sJt0XL17k7GRqly5dgo2NDfz9/aFQKJCcnIybN28C6B4PS8MxiLHo7OxEfX09wsPD4ebmBj6fj+LiYqa7+s2bN9HU1AQvL68BHa++vh6LFy/Waq9ujZPuvvrVE0I0IxQKERMTQ600OpCVlYWSkhKEhYXB0dERXV1duHbtGsrLy2FmZoawsDC2QyQaoutFf7hS1mfOnAHQnVSonD17FkB317/777+flbiMTUtLC06dOoX29naIRCIIhUJcuXIFxcXFmDRpkt6SYLaSbq6v1X39+nVm7GllZSUkEgliY2ORm5uLkpISBAQEsBwhIYNz9OhRBAUFwdbWFi0tLUhOTgafz8fIkSNhYWGBiIgIHD16FJaWlsySYZ6engOeRM3DwwP19fXsJN2tra145ZVX8Ntvv6GzsxMzZszA559/DicnJ60FQ8hQYWZmNuC7bUQz165dQ3R0tNqYICcnJ7i7uyM9PZ2Sbg6i60V/uFLW8+bNYzuEIeHSpUsQiURqaz1LpVKcO3cOly5dwqRJk/QSh76XC1Ph+lrdUqmU6V5bWVkJLy8v2NvbY9SoUTh69CjL0REyeM3Nzfjll1/Q3t4OKysreHt744knnmBuGM+ePRtHjhzBnj17IJfLERAQcNu/G6rePED3ahlHjx6FRCKBs7MzBAKB2r6DWTZswEn3G2+8gW+//RZLliyBhYUFfvjhBzz11FPYv3+/xiclZKjr6OhAaWkpfHx8es2uSO5MZ2en2nggVVk7ODigs7OTxcjIYNH1oj9cKeu2tjY4ODiAz+erxWxmZoa6ujqDb6nnitraWibh7lnO4eHhOH78uN7iYLN7OcDdlm4LCws0NzfDwsICVVVViIyMREdHB4qKimioFeG0++6775avm5iYYN68eRrdoP3qq6/A4/HUJk779ddfmZ9Vr+l8IrX9+/dj+/btTJetpUuXYvz48ZDJZDRzKCEaam9vR2ZmJpydnQ36iy0X2draoqCggJn0RlXWXl5esLGxYTk6Mhh0vegPV8o6OTkZ8+fPh4WFhVrMPB4PycnJ1L1cSwQCAWQyGQD1/xtyuXzIjOkGuJt0+/r64syZM8y17OLigubmZuTl5dHfQ0L+5sUXX9Tp8QecLV+/fh0TJ05knkdFRcHU1BQ3btyAt7e3ToIjhBBNhYeH46+//kJNTQ0cHBwglUoBdHcPnDx5MsvREUK04e9LuKh0dnb26gZIBs/NzQ0XLlzAmDFjmJbRxsZG5OfnM+Od9UG13BWN6dbMyJEjYWNjg7a2Nnh5ealdGz1npieE6H4CzgEn3QqFote6ZyYmJpDL5VoPihBCBsvZ2Rlz5sxBYWEhmpubmVaaCRMm0BwUhHBcamoqgO5ufufPnwefz0dXVxcAMLOZOzo6shmiUYmIiEBaWhqOHTvGJN3nz5+Hh4cHRo8erbc42G7pbmhoQHt7OywtLfV6fm34+xwNquvF2dmZjXAI4YRTp05BJBIhIiJCbfulS5fQ2tqKu+66S+NjDjjpViqVmDFjhlpX8ra2NsyfPx9mZmbMNlqnm5D+tbS0oKamBo2NjQCAoqIi5o/4iBEjWIzMuFhaWjITpjU0NCApKcmgu8oSQgZGdfNfqVTCxMQEAoGAafU2MzODq6sr/P392QzRqJiZmeGuu+5CS0sLKisrkZGRgYkTJ6pNVKlrUqmUuXmq77H6tra2sLS0RHt7O27cuMG52b5zc3MhFAqZHqmnT59m1uluaWmhJcMI6cfFixexaNGiXtudnJxw8uRJ3Sbd69ev77UtPj5e4xMSMlQVFRUhPT0d5ubmMDU1hYmJCW7evMmMi6OkW3tU6zVKpVK0t7fD1tYW1dXVaGpqoi51HGRqagp3d/deva2I9hl6WY8dOxZAd/IVHBwMExMTphV01KhRem8JHSqsra3B4/GYNbr1SdW1XPW3U594PB48PDxQWFiIiooKziXdxcXFGDduHACgqqoK1dXVGDNmDLKzs1FYWEjDQwnph0Qi6fPviVAoZD6TNHVHSTchZOByc3MxcuRIhISEsB2KUbtx4wbOnj0LmUym9gUtNzcXPB6Pkm4OEolEg7qrTDTHlbIODg5mflZ1AayoqIBYLIarqyuLkRkXpVKJ69evo6amBlKpFDweD5mZmczrPef60ZXW1lYA+l8uTMXd3Z1Jurmmo6MDVlZWAP63ZJifnx8cHR3x559/shwdIYbLxsYG5eXlvXqDlJWVDfqziKYdJ0RPOjs7mbFVCoUCnZ2dMDMz0+sMsENBRkYG/Pz8EBYWBhMTEyprI0B1qD9cKevU1FR4eHggMDAQHR0dSEpKgkAgQGdnJ0aNGoXAwEC2QzQKGRkZKCoqgrOzM8zNzaFQKMDn8/W63BRb47lVuDyDuampKdra2mBlZYWqqiqMHDkSCoUCUqm038kICSFAZGQkEhMTIZfL4efnBwAoKSlBUlISYmJiBnVMSroJ0RMvLy9UVVUhMDAQTU1NSEpKQlxcHI2p0rL29nYMGzaMmX+Cypo7urq6mN4Jqsl+gO7Zkk+cOIFp06Yxs4saavdnLvrjjz8QGxsLc3NztetFKBQiKSlJo3VO9aWhoYGZyKugoACdnZ2IjY1Fa2srrly5Qkm3lpSWlmLixIlwc3Nj5sfQ92epoSTdN27cYOX8d8LT0xNnz56FtbU1pFIpXF1d0dTUhOPHj7PWc4AQLpgwYQLa2tpw6NAhZtJwExMTTJw4EZMmTRrUMSnpJkRPRCIRsrOzUV9fzyQMZWVlqK2tBQAEBQWxGZ7RcHV1RUNDA43t5KADBw4way/v37+faU1TtcicOHECQPc4S1qHWXtaW1v7bPVSKBRob29nIaLbk8vlzI21+vp6AN3/LxwcHNDW1sZmaEbF1NRU75OX/R1by4WpcLmle/To0bCyskJ7ezvCw8PVblZ6enqyGBkhho3H4yEuLg5TpkxBbW0tTE1NYW9vrzahuKYo6SZET4qLi2FiYoLa2lrmrllpaSmzbiYl3drh5uaGzMxMNDU1wdbWlhkPWFNTg7a2Nr3Ouks0M2XKFGY1jKlTpzLbW1pacPHiRURFRVHrjBb1TCKqqqpgamrKtCrW1NSgtbWV9YSrPyKRCBUVFfD09ERdXR2zvaOjg3pBaNGIESOQnZ2NMWPGsBaDobR0czHp5vP5GD58eJ+vUdJNyO1JJBK0t7fD2dkZJiYmUCqVgx5ec0dJd0dHBy3DQ8gA9eyiqeqmN2nSJOryrGUXLlwAAOTk5Khtz8zMpBZSA9dz3dieP6uSKHt7e7petKjnmtdpaWlqr12+fBkikQijRo1iI7TbCg0Nxblz55CZmQk7Ozumtbu6upoZgkDunKenJ8rKyvDbb78x3/fOnj3L3CyeOXOmzmNgO+l2d3cHwM2kG/jfUqWqcdyq3itFRUWIjo5mOTpCDFNbWxv27t2LkpIS8Hg8PP/887Czs2M+C2fNmqXxMTVOuhUKBTZt2oSvvvoK1dXVyM/Ph7+/P9544w34+vriiSee0DgIQgjRlsWLF6s9Z2scIrlzqqXfVAnVjRs30NTUBAA0C70WqK6VnmO6VddLbGysQV8vXl5ecHR0REdHB5RKJTMTs7OzM/Vm0aK0tDQ0NDTAx8cHSqUSEokETk5OsLS01FsMbCfdPcd030krFxt6LlWqummi6mmnGtpGCOntyJEj4PP5WL16NbZs2cJsHzFiBI4ePTqoY2qcdP/73//Gzp078d5772HFihXM9pEjR+KTTz6hpJuQHjIyMjBy5EiYmJggIyOD2a5UKhEQEIBr166htLQUAJhJgYh22djY4N5772VaZgg3/H3pN1NTU+Tn5wMALf2mZT174XDperG0tISlpSUUCgUTsyHPuM5FlZWVmDx5MpycnKBQKDBq1Ci9l7Mq6WZzyTCg+yZgXV0dHB0dWYljMPpaqlShUEAul3PiGieELUVFRXjkkUcgFovVtjs4OKCxsXFQx9Q46d61axf++9//YsaMGXjmmWeY7aNGjcLVq1cHFQQhxqqhoQEKhYL5uT9cunNuiPLz8xEQEACBQMAkZv2hsfPc8Pel34hu1dTUIC8vj5m0SiwWIzg4GE5OTixH1je5XI6CggK1brM96aPb81BgZWXFDO/g8/l6v6mhUCiYifHYml/AzMwMTk5OqK2tRUVFBaeS7p5LlaqwUY+EcE3P1VR6am9vH/R3Eo1/q6Kios+lOBQKhdoSL4QQYNq0aX3+3NLSgvT0dERGRtLEUFpQUFAAHx8fCAQCFBQUqL2mWpPU3NwcfD6fkm6O6Ln0G10vulVaWoq0tDR4enrCy8sL169fh0KhQEpKCsaMGQMfHx+2Q+zl/PnzqK6uhqenJ0QiESorK+Hm5gZzc3O2QzMqo0aNwuXLlxEVFQWFQqH361A1sz6fz4eVlZVeztkXd3d3Juk21HkO+tJzqVIV+jwl5Pa8vb2RmZmJ6dOnM9uUSiVSU1MH3dNO46Q7NDQUp06d6vVHeO/evYiIiBhUEIQMNTKZDNXV1ZDJZGyHYhR6do/9+5rCqjGqd911l0GPUSXqei79RteLbuXk5CA8PBzBwcFoaGhAbm4uxo8fj5qaGuTk5Bhk0l1ZWYlJkybB0dERDQ0NKCwshJ+fH13jWnbu3DnI5XIcOnQIfD4fcrkcSUlJTEvpggULdHp+VddyoVDIao8wDw8PZGZmcm6t7p5LldrY2IDH46G9vR3V1dUoLi7m1A0EQvQpLi4Ou3btQmVlJeRyOf7880/U1NSgvb0dy5cvH9QxNU66161bh8ceewwVFRVQKBTYt28f8vLysGvXLhw8eHBQQRBirFSzA/+dqldIZmYm031l4sSJeovLWCkUChw+fBiTJk3qNQ6HcEvPpd9UXblUy74BoMmytKi1tZUZt9qTu7s7srKyWIjo9iwtLWnYgR70bExpbW1FdnY2goOD9dbVm+1J1FS4umxYz6VKVROnqSZSKysro6SbkH44OzvjueeeQ1paGszMzNDZ2YmQkBCMGTNm0D1ENP6LFR8fj99//x1vvvkmhEIh1q1bh8jISPz++++Ii4sbVBCEGKu/jwepqKiAqakp8wWiubkZcrmcEggt4fP5zBh6wm19Lf2WmZkJALT0m5ZZWVmhpqam1xeJ6upqVrv03krPbs9Ed3p2o2xoaEB2djbc3d311qOAku478/eeX4B67y9CSP8sLCwwefJkrR1vULeJJ02ahKSkJK0FQYixGjt2LPNzZmYmPD09ERUVhaamJuaPXnFxcZ+TNZDBCQgIwNWrVxEdHU2TxXBYz6XfaNk33QoKCsKlS5fQ2NjILCuUk5ODyspKgx02Zmdnp9btGQBOnDiht27PQ5GqhVQmkzG9tXT9t8tQkm6ur9VNCNFcaWkpLl68iIaGBtx///0Qi8XIzMyEnZ0dvL29NT7eHfXNkkgkvVqVqEsnIX27du0apk2bBj6fD0tLS0RERMDKygpBQUE4fvw4dfPSkoaGBlRXV6OqqooZw2ZjY4OsrCwIBALqxs9BqutFn2sDDyWBgYGwsLBAfn4+ysrKIBAI0N7ejpiYGIPthXP27Fm0t7dj5MiREAgEqK+vh729PczMzNgOzajIZDJcvnwZ5eXlkEqlALpvbqjGV+u6xwnby4Wp9Fyrm2va2tpw48YNtLW1QaFQQCaTwcnJCUVFRYiOjmY7PEIMUk5ODvbv34+wsDBmXDcASKVSnDp1CkuWLNH4mBon3SUlJXjuueeQnJyMjo4OZrtSqQSPx2OCIoSoUygUaGlpgVgshoWFBYYNGwYAqKur67XcDRk8U1NTeHp6qm1Ttd4Rw3a7pd/KysqYn2kWeu1QKBTIzc2Fn5+f2iythq6urg4zZsyAra0t26EYtczMTNTW1iIqKgrnzp1DZGQk2tvbUVRUhPDwcJ2f31Baurnavby6uhp//fUXRCIRmpubYWNjg9bWVgC0VCkht3Lq1CncfffdGDVqFLKzs5ntXl5eOHny5KCOqXHS/cgjj0CpVOKbb76Bi4sLXbSEDJCfnx/Onz8PiUQCa2tr1NfXg8fjMbPuEu3o2aUf6L4rWVVVBVdXV1pOyMD1t/SbUqmEXC6HQCBg/uZQ0q0dfD4feXl5zNhdrlwv1tbWai0PXIiZiyorKzF27Fg4OzsjLS0NnZ2dCAgIgJWVFUpLS3U+s72hJd21tbXMEpRckJWVheDgYIwcORL79u3DhAkTwOPxkJqaCldXV7bDI8Rg3bx5s8/PNwsLC7VGZ01onHRnZmbi4sWLCA4OHtQJCRmqRo0axXThbG9vBwCYm5sjODiYEggd6OjoQEtLC1paWnDhwgXExcVx5ovSUNXf0m80plu3nJ2dUVtbC6FQiLa2Npw7d87gr5fw8HBkZGQgLCwMSqUS586dw7Rp05iWb5onQzs6OzuZmcoFAgEyMzPh7OwMR0dHpKen6/z8LS0tANhPuh0cHJgZjCsrKwe9Tq++NTc3Y/z48QDA9EZVKBRobGxER0cHhg8fznKEhBgmkUiE+vr6Xr2pysrKBv09ROOke8yYMSgvL6ekmxAN8Xg8DB8+HMOHD0dtbS1OnDiByZMnUxKhZTKZDOnp6SgtLVXrtp+dnY3x48fTMkMcoVqa6O/1JZPJkJeXhxEjRrAUmfFxdXXF5cuX0djYyIyJNvTl2VTd+1JSUpjr/MSJEwBodnttEgqFaG1thVAohFAoZMZ137hxQ+c3NpRKpcG0dPN4PLi7u+PatWu4ceMGZ5JuExMTZu4lS0tLSCQSZkUC1WR4hJDeIiMjkZiYiHvuuQdA9w3A8vJyHD16dNAzmmv87XPbtm145plnUFFRgZEjR/b60NXHGB9CuI4SP93JyMhAbW0t7rrrLjg6OqKxsREnTpxAQ0MDMjMzaYkhjsjJyUFAQECva0UulyMnJ4eSbi1StVj2HEdv6MuzTZ06lfm5paUFFy9eRFRUFOsTbhkbX19fNDY2wtnZGb6+vqivr8exY8egVCp1PvmnRCJhEkN9rQt+Kx4eHrh27RqnxnXb29ujtrYWYrEYrq6uyMzMhIuLCwDAxsaG2a+6uvq2x1L9HiFDwV133QWlUoldu3ahq6sL27dvh4mJCWJiYjBu3LhBHVPjb/61tbUoKirC448/zmzj8Xg0kRohA1BeXo7y8nKmy9zZs2chEAgAADNnzmQzNKNx/fp1TJgwAc7OzgD+d4MjNDQUV65coaSbI/qbXLBnayzRDi4uz6a6voH/dSW3t7c36Ji5qGevRgcHBwDAyJEj4erqqvNJ7CorKwEAZmZmBnHNc3EytdGjR0MmkwHorjeZTMYk2KGhoWyGRohB4/F4mDx5MiZOnIj6+np0dnbCycnpjj6LNE66ly9fjoiICPzwww80kRohGsjPz8eVK1fg6+uLiooKmJubw8zMDM3NzQgMDGQ7PKMhl8vVZisXCARwcHCAhYUF8+WDGK79+/eDx+OBx+Ph8OHDajd1T5w4AblcDn9/f7bDNBoKhQK//PILZs6cCRsbG+Z6Ud0MNCSNjY19bm9ra4ONjQ3a2tqY7yQ0q/mdUygUOHnyJNODQPV/w93dXS/Lw1ZVVQFgf7kwFS6u1d2zW76JiQmio6PR3NyM8+fPs95lnxAuEAgEzI2/O735p3HSXVpait9++42SBEI0pFoT09vbG9euXcOMGTMgEolw5coVdHZ2sh2e0XBwcMCVK1cwbtw4CAQCiMViTJkyBWlpaUxLDTFcERERUCqVOH/+PEaMGKH2R47P58PKygqOjo4sRmhcVGWq6lkgFosxY8YMlqPq29GjR5mbMH1JTU0FYLhd4rmGz+ejqamJea7v/xuqpPtOkkOFQoGysjK0tLTA2toa3t7e4PP5gzoWl9fqlsvlkEqlUCqVEAgEzORqhJC+KRQKJCcnM6s2AN29bsaOHYspU6YM6sa0xkn39OnTkZmZSUk3IRpqa2tjkj6BQMC0uvr4+ODYsWOIjIxkMzyjMXr0aJw6dQq///4709rV2NgIgUAw6MkviP6oJigSCoVwdHQc9BdkMnAhISHIysrC2LFjDXrG8p4z2hP98Pb2RklJCSvz9dxp0p2bm4vExEQ0Nzcz28RiMWbPng17e3uNj8fF7uUtLS04f/486urq1Lareg/RzSlC+nbo0CFcvXoVsbGx8PLyAtA9RDQlJQVtbW24++67NT6mxkn3/PnzsXr1amRlZSEsLKzXRGqqWd4IIeosLCyY5VfMzMxw9OhRxMXFMbPBEu2wtbXFnDlzUFpaipaWFnR0dKCzsxPTp09XmziGGLaeY3Zv3ryJ48eP05JQOlJYWAiJRILff/8dFhYWaGtrY7oTA4Yz30R/k2lxZRw6FymVShQVFaG6uhpWVlaoqKiAt7c3M4Rn9OjROju3akz3YJLu3Nxc7Nmzp9f25uZm7NmzB/fdd5/GPWa4mHSnpaWBz+fjrrvugoWFBXg8Hpqbm3H27NlBTwZFyFBw5coVLFq0CMOGDWO2ubi4wMbGBr/88ot+ku5nnnkGAPDmm2/2eo0mUiOkf87Ozrhx4wbs7Ozg4eGBq1ev4uLFi2hpaYGnpyfb4RmN2tpaODg4ICAgAED3F/KysjLweDzU1tbCycmJ5QjJQMhkMly+fBnl5eXMjakTJ04wY3aphUZ7ei4J1t7ejuLiYjg5OcHS0pLFqAamqakJN2/eBGD4y5xxUVNTE3OjS1W2LS0taG9v1/mcPlVVVbC0tNQ46VYoFEhMTLzlPoWFhXBwcNDoPfQc061qKTZ0jY2NiIuLUxuDrxqeYShj5QkxRAKBoM+5QWxtbQc954nGSbdqvT9CiGaio6OZP3ZeXl64evUqhEIhfHx8aGIoLUpOTsb8+fPVJlMDupO4kydPUrLGEZmZmaitrUVUVBTOnj0LhUKBgIAA3Lhxg5am1LKey681NDSguLgYAQEBBt1qLJFIkJqaqjbmODMzk27KaNm0adOYn1U9CqKjo/Xyf6Oqqgp+fn4aJ91lZWVqXcr7IpVK1W4oDITqRk57e7vGv8sWsVhMvekIGYSxY8fi5MmTiI+PZ1bBkclkOHXqFMaMGTOoY9JiwYTogUKhQG5uLvz8/GBlZcVsHz58uEF/seWi/iZZ6urqMsgZmUnfKisrMXbsWDg7OzOJlL+/PxwcHFBaWgofHx+WIyRsunTpEoRCIaZOnYqDBw9CLpcjOjoaxcXFOl8/eigpLS2Fh4cH86VTnyorKweVdKuW5LwdTScwtbS0hJ2dHRoaGlBRUWGwSbdqbXMACA8Px+XLlxEWFgYbGxvw+XxmPhlazYOQ/lVVVaG4uBgff/wxs0Z9dXU15HI5/Pz88NNPPzH7PvDAAwM65oA+RT/77DM89dRTsLCwwGeffXbLfV944YUBnZiQoYTP5yMvL4+ZJIpoX8+Zi8+fP89MwKX6ApKenk6zXnOIav4DoHupG9XQJUdHR6Snp7MZmtFRKBTIz8/H9evXIZFIAHR35VddQwsWLGAxur7V1dVh6tSpMDc3Z27K2NnZISwsDJcuXTKYcehcl5GRgYsXL8Ld3X1Qk4/dicEuGTbQ/Qez/I+HhweTdPfsIWJIVMsuqiiVSqSkpKg9B7qv8cWLF+s9PkK4wMLCotda9nc6L9CAku6PP/4YS5YsgYWFBT7++ON+9+PxeJR0E9IPZ2dn1NbWQigUQiwWY86cOWqt3uTOqCbWUiqVMDExYVq1TUxM4OXlBbFYTKsucIhQKERraytzvYhEIojFYhQVFdEkalqWk5OD4uJiBAcHIysrC8OGDUNXVxdu3LjR60uHoVBd5wBgbm6OyMhIiMVitLe3D7ilk9ze/PnzUVVVhbKyMmRlZcHMzAxFRUXw9fXV6U1MuVyO2tpaAP1PoNcfb29viMXiW3YxNzc3H9QXaHd3d1y5csWgJ1ObOnXqLV9XKBTo6OjoNQSLEPI/8fHxWj/mgJLukpISnDx5EhMmTEBJSYnWgyBkKHB1dcXly5fR2NgIe3t7CAQCtS8FNPHPnRk7diyA7i9owcHBrHSHJNrj6+uLxsZGODs7IzQ0FH/99RdKS0uhVCqp+7CWlZaWIjo6Gu7u7sjOzsawYcMgEomQn5+P+vp6tsPrk42NDZqamiASieDg4ICysjKIRCIUFRVpnKSR/vH5fLi7u8Pd3R0ymQwVFRUoKytDcnIyLC0tdbaMW21tLRQKBXg8nsb1yefzMXv27D5nL1cJDAwc1ERoXFiru+fKD4SQwVH1klTd5G9sbMTVq1fh5OTETNSrqQF/K502bRoqKyvpYiZkkFRdYvPz83u9Rutlas/fu/xJJBJcuXIFI0eOHPR6r0T/goODmZ+FQiFcXFzg6uoKR0dHgx1LyVUdHR1Mq59AIEB6ejoiIyOZJNwQhYaGMmNS/fz8kJqaiuPHj8Pc3Bzjx49nOTrj1NHRgevXr8POzg6tra067VGgWi5MKBQOKjkOCQnB/fffj4MHD6K9vZ3ZfifrdAPcXDass7MTJSUlzE1+CwsLtLS0IDw8nP4mEtKPH3/8ESEhIYiOjkZHRwe2bdsGgUCAtv/H3n3Ht1Wf+wP/HEnWsC3Le2878YiT2BlkQEIWJCGEhCSshgJltVxGKKWD9rYU2l5aLvQCvbS0t5TCr6w0BFoCcUhCQibZyyuJHe8tDw1bW+f3h6ODZcuyJGv4SM/79crrFUvHR4+/J1L8nO/3+zyDg7jxxhs9KqbmctI9VnEiQohrhu+dor6y3vXFF19gyZIlXP/z4SwWCzQaDfr6+iAUCmmvJw9YrVYcOHAAs2fPhlwu55Y6T5s2jRJuHwgPD4der0dERARkMhk6OjpgMpmg0Wi4fd2TTXJyMvd3sVgMs9mMJUuWICEhgRetnPjENsNdW1uLnp4e9Pb2Ijs726fFDG37uSeSFGZmZnIJd1hYGIqLi3HLLbdAIBCgs7PTo3PyLenu7e3FgQMHIBQKuRsNbW1tMBgMSEtLo6SbkDG0t7dj5cqVAIa2YEVGRuK73/0uqqqqsH//ft8m3QDoPzJCPGA2m9HV1cX1+Dx//jzX7/TixYuQyWQoKSmhytoTkJaWBp1OB7FYPGqZvm2PJ1/6DpOh5aHDW0ER30pLS0NnZyfi4uKQkZGB/v5+HDp0CAaDAVOnTg10eHaOHz8+6jFbFepLly6hoaEBwDfbTcjEHD16FO3t7RAKhUhMTERPTw+uu+46xMTE+PQ96o2k27Y1QqFQ4Mknn/RGWLxLus+ePYvU1FTMmTOHu4HW09ODvXv34uLFi9QFgpAxmEwmSCQSAEBdXR0KCwvBMAzS09PR39/v0TndSrrvu+8+LoCxbN++3aNACAlWDQ0NaG9v55Lu2tpabo+aRqNBV1cXpFKp3XJa4p5p06Zh69atiI2NRU5ODjIzM7l9OHzpO0zsZWZmor6+nnpy+8HwMU5OTsaFCxeQkZGBxMRE7nNrsmhoaEBERITdigfb3juTyUSTA17GMAwWLFiApKQkqFQqNDc3o6WlBadOnUJfX5/PtkXZlpdPJOnu6ekBAMTFxXklJgDc+2Ey7+kerq+vzy7hBsD9fbxe5oSEstjYWNTU1KCwsBB1dXXctqWBgYFxc+GxuJV0y+VymikixE1NTU0oLCy0e6ykpASHDx/GnDlzoFarUVtbS0n3BC1duhT19fU4d+4czp07h/T0dOTk5FBBNZ5iWRZ1dXXo7OzkqvxfvHiRq7hbWloawOiCi8Fg4H6J0Ov13GOTsUp8Xl4empubMTAwwC1xHhwcxO7du1FaWko31rzM9otmd3c3ampqAAwV3svMzMSsWbN89rqetgsbzjbT7c1WZ7aZ7s7OTpjN5kn//4tIJMLg4CCioqIcPkcIcez666/HRx99hF27diEnJwcZGRkAhma9U1JSPDqnW++41157jQqpEeImrVZr15pEKBRy/f+kUilEIhHdcfaChIQEJCQkYNasWWhubkZDQwP27duHiIgI+tziEa1Wi4iICKhUKm42U6/XIzw8HAMDA9DpdDSb6SW2ZeQ6nQ6RkZGYP38+jh8/DoFAwFWpXrhw4aTqrDB79myUlpaitbUV9fX1uHDhAhITE5GZmenx7ANxTKfToaGhAfX19TCZTNwvmtdcc43PP1O9sbx8+Ez3xx9/jMbGRmzcuJH75dkTiYmJEIlEMJvN6OjoQHp6usfn8oeMjAycOHECM2fO5Fq89fT0QCQSTar3NSGTTXFxMTIzM6HRaOxqiOTm5qKoqMijc7qcdNMvOYR4xmQywWKxcF/bev/Zlryp1Wq758nEiEQi5OTkICcnBxqNBg0NDWhsbMTevXuRnJyM6667LtAhEid27tyJtWvXYunSpQCG9pSWlZVRT1kfOH/+PBQKBebPn4+GhgYcOnSI2/8JDHVcqK6unnS/nAuFQmRmZiIzMxMDAwNoaGhAQ0MDvvzyS6xcuXJSztDzzcGDB6FUKpGcnIzS0lIkJydDIBCgsbHRLzc3vLmnOzY2FpcuXYJKpUJPT8+Ekm6BQICUlBQ0NzejtbV10ifdM2fOBMMwOH78OFiWBcuyEAgEyMvLo607hIwjMjJy1GfQRP4/dLksqS+ql7/wwguYO3cu5HI5EhMTsX79ely8eNHuGL1ej0cffRRxcXGIjIzExo0bR1WdbGpqwpo1axAeHo7ExET88Ic/5FqJEBJoMpls1Ey2yWTiKgSrVCpu+SzxLrlcjvz8fKSlpUEoFHL7BMnkNfL/mvb2duj1eu79Qrynt7cX06dPR3x8PGbOnAmdToesrCxu6eyUKVN82hbKGxiGgdVqhcVigdVqDXQ4QaOjowM5OTkoKSlBamoqBAIB9/7zx+9XE93TzbIsl3THxcVxS8xts98Twad93UKhEGVlZVi/fj1uuOEG3HjjjVizZg1SUlLo/UKIn7k8071v3z6v7osBgK+++gqPPvoo5s6dC7PZjJ/+9Ke48cYbUVVVxRWa+v73v4/PPvsM//znP6FQKPDYY49hw4YNOHz4MIChdkBr1qxBcnIyjhw5gvb2dtxzzz0ICwvDf/3Xf3k1XkI8kZKSgoqKCqSkpHAVyrVaLQ4cOIBly5ahsrLS4/0hZGzd3d2or69Hc3MzLBYL0tLSRu2tJ/wwMDCAw4cPU4s9LzMajdwKgrCwMIhEIphMJm6sZTLZpLyBbbFYuOXlSqUScXFx0Ov1WLFiBc1ye8myZctw5coV7N69G1FRUcjKykJUVBRYlsXg4KBXi5M5MtGZbpZlsW7dOvT29iI6OpqL15aITwQfKpjbfkd2xGQyoaurC0lJSZDL5UhOTqbuKYT4gctJ9/XXX+/1Fy8vL7f7+u9//zsSExNx6tQpLF68GCqVCm+++Sbee+89LFu2DADw1ltvoaioCF9//TXmz5+PL774AlVVVdizZw+SkpJQWlqKX/3qV/jxj3+MX/7ylxCLxaNe12AwwGAwcF9P9jv5hN+KiorQ3NyMnTt3Ij8/H3K5HFqtFgBw5MgR7hgycTqdDvX19WhoaIBWq0V8fDwKCgpQVVWF4uJiSth4wNFWJtre5D+TfaxPnTqF5uZmyGQy5OTkYP78+VwhtckeO5/ExcUhLi4OZWVlaGpqQkNDAzdL3Nvbi+TkZJ/d4NBqtdz/kZ4m3QKBANOmTeO+tk0ahUrS7eza2FYTCQQCaLVaHD16FAUFBUhISPBXeISEpElVutDW89H24Xjq1CmYTCasWLGCO6awsBCZmZk4evQo5s+fj6NHj2L69OlISkrijlm5ciUeeeQRVFZWoqysbNTrvPDCC3juued8/NMQMkQqlWLZsmU4ffo0Lly4YLd8NiIiAvPmzaP9ql5w4MABdHZ2QiKRICsrCzk5OYiKikJfXx+qqqoCHR5xEcuyOHHiBNfWxmKxoLq6GgBw7tw57pfJa6+9NmAxBhNnYz0ZZ7/q6uoQHh6OyMhIdHd3o7u7m1v2TP8+vE8kEiE3Nxe5ubloaWnBkSNHUF9fj9raWiQlJfmkRoZtljs8PNzhxIknhs90T3S7pK+TbovFgoMHD6K9vR0pKSlYtGiR2+9FZ33q+/r60NbWhpKSEsTExKCtrQ1XrlyhpJuEvD//+c+45557IJPJ8NVXX2HhwoVevbk4aZJuq9WKJ598Etdeey1KSkoADH3wisViu36cAJCUlMR9KHd0dNgl3Lbnbc858swzz+Cpp57ivm5tbUVxcbG3fhRCRomMjMTixYthMBig1Wqh0Whw/PhxzJo1a0KFYsg3BAIBFi5ciJSUFLuepIRfsrOz7b7OysqC0WgEMJQA0PJh7xlvrMViMbKysgIQ2dhGxgx8M3NH/z58y7btb/HixdyqIl+w/e42kW1XjY2N0Ov1SE1NhVwuR3R0NBiGgdFoxMDAwITis+3p9kXSvX37dmzZsgUtLS3cY+np6Xj11VexYcMGr78eAMTHx/NifzohvqZUKmEymbike86cOf5Pup966in86le/QkREBA4cOICFCxd6vb/fo48+ioqKChw6dMir53VEIpHYVd+kdk3EX2z/9kQiESIjIyk59KKxZlwEAgGNNY84mqFRqVRQq9WYPn26Xfs9MjF8HGs+xhwsbJ+lQqEQaWlpPqtqb0u6h7fpcdfRo0dx8eJFrF69Gtdccw1EIhFXEG749kJP2H5ubyeq27dvx6ZNm0bNxLe2tmLTpk3Ytm2bV1ZwjPw/USwWT6iiOyHBIjk5Gf/617+QkZEBlmVx5MiRMVfbeLLt2qXM+Q9/+AN+/OMfIyIiAkuXLkV7e7tXezQ+9thj2LFjBw4cOGDXfiE5ORlGoxH9/f12s92dnZ3ch3FycjKOHz9udz5bdfOJfGAT4ksKhQI33XRToMMICTTW/EfX0H/4ONZ8jJmP/DXO3ki6h/fotnnwwQe5v4/sguOO8ZaXu3LukSs0LRYLtmzZ4nDpO8uyYBgGTz75JI4fPz7h2gX0fiHEsXXr1mH//v24fPkyGIZBbW3tmBM2Pku6s7Oz8dprr+HGG28Ey7I4evTomAWJFi9e7PKLsyyLxx9/HB9//DH279+PnJwcu+dnz56NsLAw7N27Fxs3bgQAXLx4EU1NTViwYAEAYMGCBfjNb36Drq4u7kaArdomLRknhBBCCOEPW7swT5Nuq9WKvr4+APB61x3gm+XlGo0GGo0Gcrl8wuc8ePCg3ZLykViWRXNzM5qamibdlg9CgkV8fDw2bdoEAHjuuedwzz33cNtqvMGlpPu///u/8b3vfQ8vvPACGIbBrbfe6vA4hmFgsVhcfvFHH30U7733Hv71r39BLpdzdzcVCgVkMhkUCgUeeOABPPXUU4iNjUVUVBQef/xxLFiwAPPnzwcA3HjjjSguLsa3v/1tvPjii+jo6MB//ud/4tFHH7VbQk7IZNLf34+vvvoK119//aiaBcS7aKz5j66h//BxrPkYMx/5a5wnuqdbrVbDYrFAKBQ63G4w0UJqcrkccrkcGo0Gra2tXmlFabvRMB5vdNuh9wsh43v22We9fk6Xku7169dj/fr10Gq1iIqKwsWLF72yvPxPf/oTAGDJkiV2j7/11lu47777AAD/8z//A4FAgI0bN8JgMGDlypX44x//yB0rFAqxY8cOPPLII1iwYAEiIiJw77334vnnn59wfIT4CsuyMBgME/7Pn4yPxpr/6Br6Dx/Hmo8x85G/xnmiy8ttS8tjYmLsloa2tLTgo48+glQqxfr16ycUY1paGmpqatDW1uaVpFupVLp0XHd3N6xW64RqlND7hQSjQ4cOYe/evZg3bx5WrVoFADCbzdi1axcqKythNpuRn5+Pm266yeUCxr29vfj666+592dCQgLmzZvn8Qoat6qhRUZGYt++fcjJyfFKITVX3vBSqRSvv/46Xn/99TGPycrKwueffz7heAghhBBCSOBMdHm5o/3cACCTydDf3++VasS2pHuiFcwbGxvxox/9CFu3bnXp+AMHDuD8+fNYsGABSktLvdZSjRA+a21txalTp0bVSigvL8fly5dx2223QSKRYOfOndi6dSvuv//+cc9ZW1uLDz74AMnJyVyhwebmZvzxj3/EXXfdhby8PLfjdDtzvv7662GxWPDRRx9x/TyLi4uxbt26SdnTkxBCCCGE8MNEl5f39vYCGL2f29Y2zGQywWAwTGgL4kR7dWu1Wvzud7/DSy+9BL1eD4FAgOXLl2PPnj0A7CelbIXT7rjjDkRERKC/vx87d+7E/v37MXfuXFxzzTVe3XdKSCBpNBq7rlIjO06NZDQasX37dqxduxYHDhzgHtfr9Thz5gw2btzI1Qxbt24dXn/9dbS0tNgV7nZk7969mD9/PlasWGH3+J49e7Bnzx6Pkm6316fU1taiuLgY99xzD7Zv347t27fj29/+NqZNm4a6ujq3AyCEEEIIIcRisaCrqwuA5zPdCcVluP62u6GYUoIqpYb7c7FvEJmzFyKxZDZ0Ot2E4vS0VzfLsjh//jwKCgrw61//Gnq9HkuXLsXp06fxxRdfYNu2baNasaWnp2Pbtm14//338eSTT+Lmm29GbGwsdDodDhw4gFdeeQU7duzgZvgJ4bPi4mIoFAruzwsvvOD0+M8//xxTpkxBbm6u3ePt7e2wWq12j8fHx0OhUKC5uXncOLq7u1FWVjbq8bKyMnR3d7v409hze6b7iSeeQG5uLo4ePcrdRezp6cHdd9+NJ554Ap999plHgRASSiIjI7Fs2TKX95UQz9FY8x9dQ//h41jzMWY+8sc49/T0wGKxgGEYJCQkcLPW7pCFh0MWHj7mczq9HjqdbkJFxDzp1d3S0oLy8nK0traira0Nubm5eOmll7B+/XpuNnvDhg1Yt24dDh48iPb2dqSkpGDRokXcSlKRSITZs2dj1qxZqKmpwZEjR9DS0oJTp07h9OnTKCwsxJw5c7gOP47Q+4VMZlVVVXY3npzNcldUVKC9vR0PPfTQqOe0Wi2EQiGkUqnd4xEREdBqtePGERERgY6OjlHbVDo6OjxeWeJ20v3VV1/h66+/tlu2ExcXh9/+9re49tprPQqCkFATFhaG+Pj4QIcREmis+Y+uof/wcaz5GDMf+WOcbfu54+PjvbL3eiSZTAYAE57pdmd5uUqlwt69e3HhwgUAQ0nE7373O2zZssVhQiEUCkcVGB6JYRgUFRWhqKgITU1NOHz4MC5duoTq6mo88cQTuPbaa/GjH/0IN998s13RNYvFgsOHDztM6AmZDORyOaKiosY9TqVSoby8HN/+9re9UmdspFmzZmHHjh3o6+uz29N9+PBhroOWu9yOUiKROGxZoNVqqaADIS4aHBzEpUuXMHXqVISPcUeeeAeNNf/RNfQfPo41H2PmI3+M80T3c2u1WrS0tCAiPAIxsTGjnvdn0m0ymXD48GEcOXIEJpMJDMOgrKwMy5YtG7UUdiIyMzORmZmJ7u5uHD16FGFhYTh8+DDWrVuHwsJCPP3007j77rvx2WefYcuWLRgcHMSaNWvw2WefITw8HK+++ipNmhHeaW9vx8DAAP785z9zj7Esi8bGRhw/fhx33303LBYL9Hq93Wz3wMCAS6s8Fi9eDLFYjKNHj2Lv3r0Ahm4IXH/99Zg3b55HMbuddN988814+OGH8eabb+Kaa64BABw7dgzf+973cMstt3gUBCGhxmAw4NKlS8jKyqJfEn2Mxpr/6Br6Dx/Hmo8x85E/xnmi7cLa29tRV1eHiIgIzImdM+r5iIgIREVFISLC8yJqALgqya2trdi7dy+WLFnCzRizLIuKigrs2bOHKwiVlZWFVatWefxzuSIhIQG33HIL1qxZg9deew1vvPEGampq8OCDD+Lpp59Gf38/ACAnJwc333wzDh06hIaGBmzatAn79u3zSuszQvwlJycHjzzyiN1j//rXvxAfH49rr70WUVFREAgEuHLlCoqLiwEMteZTqVTczLUzDMNgwYIFWLBgAQwGAwDnS91d4XbS/dprr+Hee+/FggULuKU/ZrMZt9xyC1599dUJBUMIIYQQQkKTt9qFhV+d0R4pOjoaZWVliLMMehYggO3bt2PLli0AhhLsFStWID09Ha+++ipSU1Px5ZdfoqWlhXu9G2+8EUVFRR6/nrtSU1Px29/+Fj/96U/xf//3f/if//mfMWfkWZYFwzAoLy9HQUEBt7eckMlOIpEgMTHR7rGwsDDIZDLu8bKyMnzxxReQyWRcy7D09PRxK5c7ei1vcDvpjo6Oxr/+9S/U1tZyLcOKioqQn5/vlYAIIYQQQkjomehMty3plo6RdE/U9u3bsWnTJruWXsDQjPfGjRsBAI888gjEYjEWLVqE+fPn+2S/qSuioqLwgx/8ADNmzMCNN9445nEsy0KlUqGpqQlZWVl+jJAQ31q1ahV27dqFrVu3wmKxIC8vD2vWrAlYPB5/EuTn51OiTQghhBBCvMIrPbrDY8Zd/s6yLFiWtSsyNh6LxYItW7aMSrht57MpLS3F8uXLJ011cKVS6dJxjuo1EcIn9913n93XIpEIa9asCWiiPZzbfboJIRMnFouRl5dHxQf9gMaa/+ga+g8fx5qPMfORP8Z5ojPdthZjMunYM90XL17EwYMH0dnZ6da5Dx48yC0bd6a0tHTSJNzA6BsYarUau3bt4vab28jlcn+GRUjIoaSbkACIiIjA7NmzPe71R1xHY81/dA39h49jzceY+cgf4zyRPd1msxkqlQoAIAsfO+kWCoVgWRaDg+7t67bFNp7JNmO8aNEipKenc/u1e3p68Le//Y1bis8wDBQKBTIzMwMZJiGThsViwTvvvMO9R7yFkm5CAsBsNqOvrw9msznQoQQ9Gmv+o2voP3wcaz7GzEf+GOeJLC/v6+sDy7IQCoUQO+nxbVt67m7bMFdjmmwzxkKhkCt0zDAMxGIxcnJyIBaLuUR81apVVESNkKuEQqHbK2FcQUk3IQGg0Wiwe/fuSXdHPBjRWPMfXUP/4eNY8zFmPvL1OA8ODnJLnj2Z6Y6JicGDDz44VCncSQJpW3rubtI9csZ4JIZhkJGRMSlnjDds2IBt27YhLS0NaWlp+O1vf4u0tDSkp6dj27Zt1C6MkBGmT5+OM2fOePWcHhVSO3jwIP785z+jrq6OexP/v//3/5CTk4PrrrvOqwESQgghhJDgZpvllkqliIqKcvv7RSIR0tLSoFI6vylgW3qu1+u5llmusM0Yb9q0CQzD2BVPs53jlVdembQzxhs2bMC6devw3HPPAQCKi4vxr3/9y2ezeoTwmdVqxcmTJ3HlyhWkpKSMqmWxcuVKt8/p9kz3Rx99hJUrV0Imk+HMmTNcw3CVSoX/+q//cjsAQgghhBAS2oYXUfNl4iqRSCAQCGC1WrnfYV01fMZ4ONuM8YYNG7wZqtcJhUJcf/31AID+/n4IhcIAR0TI5NTd3Y2UlBRIJBL09vaio6PD7o8n3J7p/vWvf4033ngD99xzDz744APu8WuvvRa//vWvPQqCEEIIIYSErom2Czt27BgAQJyeB4lEMuZxDMNAKpVicHAQOp0OUqnUrdexzRgfPHgQ7e3tSElJwaJFi3iTwGZnZ6OnpwdtbW3Q6XSQ+ainOSF8du+993r9nG4n3RcvXsTixYtHPa5QKNDf3++NmAgJCSKRR7s7iAdorPmPrqH/8HGs+RgzH/lynCfaLuzw4cPQaDRY+e0HnSbdABAXFwe5XO7xzyMUCrFkyRKPvjfQYmNjodPpYLVaUVtbi+nTpwc6JEImrd7eXvT29iIrKwthYWFubUkZye1Pm+TkZNTW1iI7O9vu8UOHDiE3N9ejIAgJNTExMZN+GVqwoLHmP7qG/sPHseZjzHzk63GeSLswk8nEFXhzZeY2lH9fjYmJwRtvvIHGxkbU1NRQ0k2IA4ODg9i2bRvq6+vBMAwef/xxxMTE4N///jekUql/9nQ/9NBD2LJlC44dOwaGYdDW1oZ3330XTz/9NB555BG3AyCEEEIIIaFtIsvLe3t7AQwVYQtz0i6MDCkoKAAwtHqVEDLarl27IBAI8P3vf9/uM2XatGmoq6vz6JxuJ90/+clP8K1vfQvLly+HVqvF4sWL8eCDD+K73/0uHn/8cY+CICTUqFQqlJeXQ6VSBTqUoEdjzX90Df2Hj2PNx5j5yNfjPJHl5T09PQCGlo27imVZtwupBQOVSoUVK1YgLS0NNTU1gQ6HkEmprq4OK1asGNVJIS4uzuPt1G4vL2cYBj/72c/wwx/+ELW1tdBqtSguLkZkZKRHARASiqxWK9RqNaxWa6BDCXo01vxH19B/+DjWfIyZj3w9zhNZXu5u0m00GvH1118DGOq/PVnbfPmC1WqFWCyGWCympJuQMZhMJoerZnQ6nce1INye6bYRi8UoLi5GYWEh9uzZg+rqak9PRQghhBBCQthEZrpty8tjY2NdOl4sFnO9tkNxttvm4sWLdv3GCSFDMjMzce7cObvHWJbF4cOHR9U1c5Xbqfrtt9+OxYsX47HHHoNOp8PcuXNRX18PlmXxwQcfYOPGjR4FQgghhBBCQo/VakVnZyeAie3pdjXpBoYKrg0MDGBwcNDttmHBQCAQQKvVoq2tjar/EzLCDTfcgHfeeQft7e2wWCzYs2cPurq6oNPpcP/993t0Trdnug8cOIBFixYBAD7++GNYrVb09/fjtddeoz7dhBBCCCHELb29vTCbzQCAxMREt7//jjvuwP3334+8vDyXv8dW5Vyv17v9esEgLS0NABVTI8SRxMREPPbYY8jIyEBBQQGMRiOKiorw3e9+162be8O5fWtLpVJxL1ZeXo6NGzciPDwca9aswQ9/+EOPgiAk1ERERODaa69FREREoEMJejTW/EfX0H/4ONZ8jJmPfDnOtv3ccXFxEIvFbn9/eHg4wsPDh74Y1Lj0Pbake3Bw0O3X4zPbdfzwww8BADU1NZg2bVqAoyJk8pFKpVi8eLHXzud20p2RkYGjR48iNjYW5eXl+OCDDwAAfX19Ibk8hxBPiMVi7i4z8S0aa/6ja+g/fBxrPsbMR74c54m0C/OULenW6XR+e83JwHYdbb3KaaabEMd0Oh3OnDmD7u5uAEBCQgLKysq4zw53uZ10P/nkk9i8eTMiIyORlZWFJUuWABhadj59+nSPgiAk1Oh0OjQ0NCA7O9vjNy9xDY01/9E19B8+jjUfY+YjX47zRIqoNTY2orq6GtnZ2SgsLHT5+0I16bZdx6KiIgDgdQVzWx0AZ5KSkvwQCQk2jY2NeP/99yGRSJCamgoAOH78OA4cOIC77roLWVlZbp/T7aT7P/7jPzBv3jw0NTXhhhtugEAwtC08NzeX9nQT4iK9Xo8LFy4gOTmZfkn0MRpr/qNr6D98HGs+xsxHvhpni8WCQ4cO2X0tFApd/v6GhgYcO3YMBoPBraQ7PDwciYmJ3yxLDxG262irwEwz3YSM9vnnn2PatGlYs2YNl+tarVZ89tln+Pzzz/HII4+4fU6PWobNnj0bt956q11v7jVr1uDaa6/15HSEEEIIISTEbN++HdnZ2fjLX/4CANizZw+ys7Oxfft2l8/hSeVyYGiZdVFRkUczVsEgJycHwNCMnslkCnA0hEwuvb29WLBgAZdwA0MV/xcsWMB95rjLox4BLS0t+Pe//42mpiYYjUa7537/+997FAghhBBCCAkN27dvx6ZNm0b1iW5tbcWmTZuwbds2lyZzenp6AAwVYSOui46ORmxsLHp7e9Hb20vLsAkZJiUlBUqlEvHx8XaPK5VKj98rbifde/fuxS233ILc3FzU1NSgpKQEDQ0NYFkWs2bN8igIQgghhBASGiwWC7Zs2TIq4QYAlmXBMAyefPJJHD9+HAzDOD2XpzPdttcyGAxgGAYSicTt7+e7wsJCHDlyZEKJBCHBYniNgGuuuQbl5eXo7e1Feno6gKFJ5xMnTmD58uUend/tpPuZZ57B008/jeeeew5yuRwfffQREhMTsXnzZqxatcqjIAgJNWFhYUhPT0dYWFigQwl6NNb8R9fQf/g41nyMmY+8Oc4HDx5ES0vLmM+zLIvm5mY0NTU5Xf5tMpm4QmieJN0NDQ1oampCWloa8vPz3f5+Php+HYcn3YSEujfeeAMMw9jdDNy9e/eo47Zv346SkhK3z+920l1dXY33339/6JtFIuh0OkRGRuL555/HunXrPNpYTkioiYyMxMKFCwMdRkigseY/uob+w8ex5mPMfOTNcbb15R6PRuO857Yt4ZbL5R719w7FCubDr2NBQQGAb5boExLKtmzZ4tPzu510R0REcPu4U1JSUFdXh2nTpgEA3SkjxEUWiwUGgwESicStKq3EfTTW/EfX0H/4ONZ8jJmPvDnOrvbjlsvlTp/X6/UAPN/PbUu6BwcHPfp+Php+HW3V3un3d0KG6hz4ktvVy+fPn8+1drjpppvwgx/8AL/5zW9w//33Y/78+V4PkJBgpFarsWPHDqjV6kCHEvRorPmPrqH/8HGs+RgzH3lznBctWoT09PQx92szDIOMjAxkZmY6PU9iYiKefvpprF271qM4bEm3wWBwuL88GA2/jraZbqVSGTI/PyGu0mg0qKysxPHjx3Hs2DG7P55we6b797//PbRaLQDgueeeg1arxYcffogpU6ZQ5XJCCCGEEOKUUCjEq6++ik2bNo16zpaIv/LKK+MWUQOGVmBGRER4FIdYLIZQKITFYoFOpwu5nt25ubkQiUQwmUzQaDSIiooKdEiETApnz57Fjh07IBQKIZPJRn0WzZs3z+1zup105+bmcn+PiIjAG2+84faLEkIIIYSQ0LVhwwZs27YNDz74IPr6+rjH09PT8corr2DDhg121YR9RSaTQavVhmTSHRYWhry8PABD+7op6SZkyL59+7B48WIsWrTIpZt/rnB5efnx48dhsVjGfN5gMGDr1q1eCYoQQgghhAS3DRs24LHHHgMALF26FPv27UN9fT02bNjg0vdXV1fjiy++4PZ2eyIUi6kNR/u6CRnNZDKhpKTEawk34EbSvWDBArvqhlFRUbhy5Qr3dX9/P+666y6vBUYIIYQQQoJba2srgKGke8mSJS4XaTMajejq6sLRo0chErm9cJOTkJCAzMzMkJ3lHb6vmxAypKysDFVVVV49p8ufUiMLLDgquEBFGAhxTXR0NDZu3AiBwO1ahsRNNNb8R9fQf/g41nyMmY98Nc7Nzc0AgIyMDLe+zzYzHR0dPeGkOyEhwePv55uR17GwsBAnTpygpJuQYZYvX473338ftbW1SExMHHUzcOXKlW6f0/NPKQe8OQVPSDBjGIZa2/gJjTX/0TX0Hz6ONR9j5iNfjfNEk+7Y2FivxxTMRl7HgoICSroJGeHQoUOora1FfHw8urq6vJLjejXpJoS4RqPR4OTJk5gzZ864fUjJxNBY8x9dQ//h41jzMWY+8sU4syw7KZJug8EAnU4HhUIR9BNII6+jbXm5Wq2G0WiEWCwOcISEBN7Ro0exbt06lJaWeu2cbiXdVVVV6OjoADD0QVlTU8O1D6M7ZIS4zmw2o7u7G2azOdChBD0aa/6ja+g/fBxrPsbMR74Y5/7+fgwMDAAYqlruDlvSHRcXN+E4Tp48CbPZjDlz5njcfowvRl7HuLg4REREYGBgAL29vUhOTg5whIQEnlAodPtG4HjcSrqXL19ut2/75ptvBjC0VIVl2aC/O0gIIYQQQryjpaUFwNBstbvturyZdMtkMmg0Guh0uqBPuh2Ji4vDwMAAlEolJd2EYKgP9/Hjx7F69WqvndPlpLu+vt5rL0oIIYQQQkKbp0vLgaEl4YB3lpfbku6JtB7js/j4eDQ1NdGqVUKuamtrQ319PS5duoTExMRRBSTvuOMOt8/pctKdlZXl9skJIYQQQghxZCJJ98KFC2E0GhETEzPhOGy9ugcHByd8Lj6Kj48HALvWwISEMqlUiqKiIq+ekwqpERIA4eHhmDNnjtvL6Yj7aKz5j66h//BxrPkYMx/5YpwnknQDgFgs9koLM1vSbVuyHswcXUdb0k0z3YQMWbdundfPSUk3IQEgkUiQm5sb6DBCAo01/9E19B8+jjUfY+YjX4zzRJNubwmlpNvRdbTti+/p6aEaTYT4CCXdhASAwWBAa2sr0tLSIJFIAh1OUKOx5j+6hv7Dx7HmY8x85Itx9jTpbmlpgUqlQnJyMpKSkiYchy3pNhgMsFqtXpk9n6wcXcfo6GgIhUKYTCao1WooFIoAR0lIYL366qtOn9+yZYvb56Skm5AAGBwcxMmTJxETE0O/JPoYjTX/0TX0Hz6ONR9j5iNfjLOterm77cJUKhWUSqVX9nMDQFhYGLKysiCVSr1yvsnM0XUUCASIjY1Fd3c3lEolJd0k5M2bN8/ua6vVio6ODtTW1mLhwoUendPtpHvZsmXYvn07oqOj7R5Xq9VYv349vvzyS48CIYQQQgghoYFlWY9num0Fz2wz1N6QnZ3ttXPxUXx8PLq7u9HT04O8vLxAh0NIQM2fP9/h48ePH0d7e7tH53R7/cz+/fthNBpHPa7X63Hw4EGPgiCEEEIIIaGjp6eHa9Hlzkw3y7Lc93kz6Q51tn3dVEyNkLFNmTIFVVVVHn2vyzPd58+f5/5eVVWFjo4O7muLxYLy8nKkpaV5FAQhhBBCCAkdtlnuxMREt5arD9937c3tBGazGVqtFgzDhOTyaqpgTsj4qqqqPL7Z53LSXVpaCoZhwDAMli1bNup5mUyGP/zhDx4FQUioEYlESEhIgEhEZRV8jcaa/+ga+g8fx5qPMfORt8fZ06XltgrjUqnUq1W2e3t7UV1djejoaMycOdNr551sxrqOlHQT8o0///nPox7TarUYGBjAmjVrPDqny5+c9fX1YFkWubm5OH78OBISErjnxGIxEhMTIRQK3XrxAwcO4L//+79x6tQptLe34+OPP8b69eu55++77z68/fbbdt+zcuVKlJeXc1/39vbi8ccfx6effgqBQICNGzfi1VdfRWRkpFuxEOJPcrkcS5cuDXQYIYHGmv/oGvoPH8eajzHzkbfHeaJJt7f7stuKqAV727CxrqNteblGo4HRaIRYLPZ3aIRMGgUFBXZfMwyDiIgIZGdnczeo3OVy0p2VlQVgqHqbtwwMDGDmzJm4//77sWHDBofHrFq1Cm+99Rb39cilRJs3b0Z7ezt2794Nk8mE73znO3j44Yfx3nvveS1OQryNZVlueRz1w/QtGmv+o2voP3wcaz7GzEfeHmdPK5dbLBYIBAKv7+e2JfEGgwEWi8Wr555MxrqOUqkUkZGR0Gq16OnpQUpKSgCjJCSwlixZ4vVzupR0//vf/8bq1asRFhaGf//7306PveWWW1x+8dWrV2P16tVOj5FIJEhOTnb4XHV1NcrLy3HixAnMmTMHAPCHP/wBN910E1566SWkpqY6/D6DwQCDwcB9rdFoXI6ZEG/o7+/H7t27ccMNN3it5QlxjMaa/+ga+g8fx5qPMfORt8fZ05nujIwMZGRkeHUSCBhadh0WFgaTyRTUs93OrmNcXBy0Wi2USiUl3YR4mUtJ9/r169HR0YHExES75d8jMQzj9buD+/fvR2JiImJiYrBs2TL8+te/5pbAHD16FNHR0VzCDQArVqyAQCDAsWPHcOuttzo85wsvvIDnnnvOq3ESQgghhBDXeJp02wgEbjfgGZdMJgv6pNuZ+Ph4NDY20r5uErKee+45l1by/OIXv3D73C4l3cPvJnr7zqIzq1atwoYNG5CTk4O6ujr89Kc/xerVq3H06FEIhULuRsBwIpEIsbGxdtXVR3rmmWfw1FNPcV+3traiuLjYZz8HIYQQQgj5xkSTbl+QyWRQq9UhnXQDQ+3cCAlFd9xxx5jPtbS04NixY2BZ1qNzT+pSn3feeSf39+nTp2PGjBnIy8vD/v37sXz5co/PK5FI7PaGq9XqCcVJCCGEEEJcY7VauT3d7iTder0elZWViIyMHFXoyBts+8RDPemmmW4SqgoLC0c9plQqsXfvXly8eBEzZszweL+320n3E088gfz8fDzxxBN2j//v//4vamtr8corr3gUiCtyc3MRHx+P2tpaLF++HMnJyejq6rI7xmw2o7e3d8x94IQQQgghJHC6u7thMpnAMMyY9XccGRwchFar9VlccXFxEIvFIdsBx7Z9s6enByzLUmFCMmmdOHECJ0+eRH9/PwAgMTERixcvxpQpUwAM5YO7du1CZWUlzGYz8vPzcdNNN7n13tZoNNi3bx/OnTuH/Px8fO973xu1wtodbm+I+eijj3DttdeOenzhwoXYtm2bx4G4oqWlxa6i4oIFC9Df349Tp05xx3z55ZewWq2YN2+eT2MhZCKioqJw8803IyoqKtChBD0aa/6ja+g/fBxrPsbMR94cZ9vS8uTkZISFhbn8fbYZaG9XLreJjIxESkoK5HK5T84/GTi7jtHR0RAKhTCbzVCpVAGIjhDXREVFYcWKFXj44Yfx8MMPIzs7Gx988AE3GVteXo5Lly7htttuw3333QeNRoOtW7e6dG69Xo/du3fjtddeQ3d3N+655x7cddddE0q4AQ9munt6eqBQKEY9HhUV5fZyFK1Wi9raWu7r+vp6nD17FrGxsYiNjcVzzz2HjRs3Ijk5GXV1dfjRj36E/Px8rFy5EgBQVFSEVatW4aGHHsIbb7wBk8mExx57DHfeeadbd04J8TehUOj1HqPEMRpr/qNr6D98HGs+xsxH3hznifbodjXpZlkWKpWK6zutUChCfvbW2XVkGAZxcXHo6uqCUqlEdHS0f4MjxEUjt5csX74cJ0+eREtLC6KionDmzBls3LgROTk5AIB169bh9ddfR0tLi9M2hYcPH8bhw4cRGRmJjRs3Olxu7im3k+78/HyUl5fjscces3t8586dyM3NdetcJ0+exNKlS7mvbcXN7r33XvzpT3/C+fPn8fbbb6O/vx+pqam48cYb8atf/cpuP/a7776Lxx57DMuXL4dAIMDGjRvx2muvuftjEeJXWq0W58+fx4wZM0J2GZu/0FjzH11D/+HjWPMxZj7y5jj7I+lWKpWora21axErkUiQn5+PuJixbx5oNBoMDAxAJpMF5eqJ8a5jfHw8urq60NPTg/z8/ABESEKZRqOxq7U1sg6XI1arFVVVVTCZTMjIyEB7ezusVqtdXhofHw+FQoHm5manSfeePXsQFhaG2NhYnDt3DufOnXN4nLOCa2NxO+l+6qmn8Nhjj6G7uxvLli0DAOzduxcvv/yy2/u5lyxZ4rQC3K5du8Y9R2xsLN577z23XpeQQDOZTGhpaUFRUVGgQwl6NNb8R9fQf/g41nyMmY+8Oc6eJt2Dg4MAxk+6lUolKisrRz1uMBhQWVmJuMIcrmjYSHV1dVCpVBAKhSgpKXErPj4Y7zpSMTWgs7Nz3GOSkpL8EEnoGdlN6tlnn8Uvf/lLh8d2dnbizTffhNlshlgsxh133IGEhAR0dHRAKBRCKpXaHR8RETFuTYiZM2f6bDWM20n3/fffD4PBgN/85jf41a9+BQDIzs7Gn/70J9xzzz1eD5AQQgghhAQPT5Juq9XKzVo7S7pZlrXbuuhIbW0t4uLiHP5yLZPJoFKp0Nvb63JswcRWTC2Uk24SOFVVVUhLS+O+djbLHR8fj+9973vQ6/WoqqrCJ598gvvuu29Cr79+/foJfb8zHrUMe+SRR/DII4+gu7sbMpmMlnMRQgghhBCXeNIuzGQyQSaTwWQyQSwWj3mcSqWyW1LuiMFggEqlcrhn2ZbQh2rSTTPdJJDkcrnL2zqEQiFiY2MBAKmpqWhra8PXX3+NkpISWCwW6PV6u9nugYGBgOasE+rTnZCQ4K04CCGEEEJICLDNdDvbWzmSRCLB3Llzxz3OaDS6dL6xjgv1pNs2063VamEwGMbdT0vIZMGyLCwWC1JSUiAQCHDlyhVuubpSqYRKpXJ7S4s3uZR0z5o1C3v37kVMTAzKysqcrnU/ffq014IjJFhJpVJMnz591H4T4n001vxH19B/+DjWfIyZj7w1zhaLBa2trQDc39PtCmez4K4cZ0u6e3p6vBbTZDLedZRIJJDL5dBoNOjp6aFuQGRS2rNnD6ZMmQKFQgGDwYALFy6goaEBd999N6RSKcrKyvDFF19AJpNBIpFg586dSE9Pd+tGn7e5lHSvW7eOu9Ply7XuhIQKmUxGRX/8hMaa/+ga+g8fx5qPMfORt8a5o6MDFosFQqEQKSkpXojMnkKhgEQicbrEXCKROGx/C3yTdA8ODo5anhoMXLmOcXFx0Gg0UCqVlHSTSWlgYAAff/wxtFotJBIJkpKScPfddyMvLw8AsGrVKuzatQtbt26FxWJBXl4e1qxZE9CYXUq6n332WYd/J4R4xmg0oru7GwkJCS7flSeeobHmP7qG/sPHseZjzHzkrXG2LS1PTU2FUCh0+fsqKythMBiQl5c3ZsIMDPWazs/Pd1i93CY/P3/MVZtCoZD7+Xp7e4Mu6XTlOsbHx6OhoYH2dZNJa926dU6fF4lEWLNmTcAT7eEEgQ6AkFA0MDCAw4cPY2BgINChBD0aa/6ja+g/fBxrPsbMR94aZ0/bhWk0Gmg0Gpfa+cTHxzucRRcJhZg2bdqY7cJspkyZgrvvvpvb3xxMXLmOVEyNEO9zaaY7JibG5Z5loVp4ghBCCCGEOOdJ5fLh7cLCw8Nd+h6zyQQASE5OBjC0rF0ikSA+Lg6w6px+b3x8fEj3YbYl3cG6r52QQHAp6X7llVd8HAYhhBBCCAl2nlQu1+mGkuSwsDCIROP/6sqyLPr6+gAAqSmpkIXL0NXVhYHBQWi0WsSFu76sPRTZZvh7enrAsqzLE2+EkLG5lHTfe++9vo6DEEIIIYQEOU+Wlw8ODgL4psjZeDRqDcwWC0QiESLlkWAYBvHx8ejq6kJHRweyc9Ocfr/JZMK5c+dgMpkwZ84cl+MMFgqFAiKRCGazGf39/YiJiQl0SITwnkd9ui0WCz7++GNUV1cDAIqLi7Fu3TqX7j4SQgCBQICoqCgIBFRWwddorPmPrqH/8HGs+RgzH3lrnD1JuvV6PQDXk+7evqGtjsO3RyYnJaOrqwtdXV2wZqc4/TkMBgM++eQTyGSyoEu6XbmODMMgLi4OnZ2dUCqVlHQT4gVuZ8mVlZW45ZZb0NHRgYKCAgDA7373OyQkJODTTz9FSUmJ14MkJNgoFAqsWrUq0GGEBBpr/qNr6D98HGs+xsxH3hpnf8x0919dWj48WYyOieZaifX09CAhIWHM77e9jk6ng06nc/l1J6JKqRn3GG+UdXP1OsbHx6OzsxM9PT2YMmWKF16ZkNDm9u3KBx98ENOmTUNLSwtOnz6N06dPo7m5GTNmzMDDDz/sixgJIYQQQgjPmUwmtLe3A3Av6RaLxQgPD3epiJrZbIZarQZgn3QzDMMVR+vo6HB6DqFQCLlcDiB0i4lRBXNCvMvtpPvs2bN44YUX7D7IYmJi8Jvf/AZnzpzxanCEBKu+vj5s376dK/RCfIfGmv/oGvoPH8eajzHzkTfGub29HSzLIiwsDImJiS5/X05ODubOnet0dtqmv78fLIBwmQxSqdTuOVsl897eXq4a+lhiY2O5Y4OJq9fRVkyNkm5CvMPtpHvq1Kno7Owc9XhXVxfy8/O9EhQhocBsNgc6hJBBY81/dA39h49jzceY+Wii42xbWp6WluazPfi2ZDLawT5kmUyGaIUCABz+LjucLekOxpluV64jzXQT4l1uf+K98MILeOKJJ7Bt2za0tLSgpaUF27Ztw5NPPonf/e53UKvV3B9CCCGEEEIAz/Zzu6vv6sy0LWkeKWlY325nbDO9wTbT7Srbzz8wMMAVsiOEeM7tQmo333wzAOD222/nKkKyLAsAWLt2Lfc1wzCwWCzeipMQQgghhPCYJ0l3d3c3Ll26hISEBEydOtXpsb29vdDp9WAYhpvRHikhIQF9QiF0Oh1UKhUUYxwXrMvLXSUWixEVFQW1Wo2enh5kZWUFOiRCeM3tpHvfvn2+iIMQQgghhAQxTyuXm81mboLHmStXrgAAFFFREI7RxlYoFCIhIQEdHR3o6OgYM+nOzMzE5s2buRnfUBQXFwe1Wk1LzAnxAreT7uuvv94XcRASUuRyOW644QauOirxHRpr/qNr6D98HGs+xsxH3hhnT5JunU4HwLV2YVeuXAGE4Q73cw+XnJyMjo4OdHd3Iz8/H0KhcNQxERERQVmryJ3rGB8fj/r6ekq6CfECt5NuANDr9Th//jy6urpgtVrtnrvlllu8EhghwUwkEtl1ACC+Q2PNf3QN/YePY83HmPnIG+Pc0tICwDdJt9VqxZUrV6CYUoLYGMf7uW0UCgVkMhl0Oh26u7u5quahwJ3rSMXUCPEet5Pu8vJy3HPPPQ7fgLSPmxDXDAwMoKamBoWFhYiIiAh0OEGNxpr/6Br6Dx/Hmo8x85E3xtk2052enu7y97iadLe2tsJgMEAkEiFSHjnueZOTk1FfX4+Ojo4xk+4rV66gqakJOTk5QbOn2Z3raEu6g7GCOyH+5nb18scffxy33XYb2tvbYbVa7f5Qwk2Ia4xGI+rq6mA0GgMdStCjseY/uob+w8ex5mPMfDTRcTYYDFybLldnus1mM0wmE4Dxk+66ujoAQExMDFfo15mkpCQwDAOVSsUl9iNVVlbiq6++4s4dDNy5jsMruFNbPkImxu2ku7OzE0899RSSkpJ8EQ8hhBBCCAkyra2tAACpVMrNoI5ncHAQACCRSBzuux7OVkTN1aXTEomEO3asnt2h3jYsKioKYWFhsFgsqK+vD3Q4hPCa20n3pk2bsH//fh+EQgghhBBCgtHwpeWuzEQDQ9sWY2NjER0d7fQ4vV7P7Rd3Z9958rCe3Y6qo4d62zCGYbgbDzU1NQGOhhB+c3tP9//+7//itttuw8GDBzF9+nSEhYXZPf/EE094LThCCCGEEMJ/nhRRk8vlmD59+rjHNTQ0gGVZxMXFQSqVunz+uLg4iEQiGAwG9Pf3j0rYbQlnT08PWJZ1+WZBMImPj0dHRwdqamqwdu3aQIdDCG+5nXS///77+OKLLyCVSrF//367DyCGYSjpJsQFEokEU6dOhUQiCXQoQY/Gmv/oGvoPH8eajzHz0UTH2ZN2Ya6y7bnOzc116/sEAgGSkpLQ2tqKjo6OUUm37Wuj0YiBgQFERo5foG2yc/c62rYCXLx40ZdhERL03E66f/azn+G5557DT37yEwgEbq9OJ4QACA8PR2lpaaDDCAk01vxH19B/+DjWfIyZjyY6zp5ULrdYLOPu5Qa+Sbrz8vLgbklfW9KtVCphNpshEn3zq7FIJIJCoYBKpUJvb29QJN3uXkdaXk6Id7idNRuNRtxxxx2UcBMyASaTCUqlkqvKSnyHxpr/6Br6Dx/Hmo8x89FEx9ndmW6WZXH06FEcOXIEer1+zON6e3vR19cHgUCA7Oxst+OSy+WIiIiA1WpFV1fXqOeHLzEPBu5eR5rpJsQ73M6c7733Xnz44Ye+iIWQkKHVavHll19Cq9UGOpSgR2PNf3QN/YePY83HmPloouPsbtI9MDAAi8UCk8kEsVg85nG2quUZGRkeL30fXlBtpJUrV+Kxxx7DjBkzPDr3ZOPudbTddFAqlUFz44GQQHB7ebnFYsGLL76IXbt2YcaMGaMKqf3+97/3WnCEEEIIIYT/3E26bRXDpVKp09WVtqTb3f3cwyUlJeHKlSvQaDQYGBhAREQE91xiYqLH5w0GYWFhUCgUAIZmuxcuXBjgiAjhJ7eT7gsXLqCsrAwAUFFRYfdcKFZ1JIQQQgghY9PpdNwsqS3prlJqnH5Ph9oAAJDJZGMew7Isl3Tn5eV5HF9YWBji4uKgVCrR0dExoXMFo+H7uinpJsQzbifd+/bt80UchBBCCCEkCNnahUVERIzbc9tmcHAQgPOkW6PRwGAwQCqVIiUlZUIxJicnQ6lUoqury27W3Gg04uuvv0Z/fz/Wrl0bkhNMtK+bkImjamiEBADDMJBIJCH5n7e/0VjzH11D/+HjWPMxZj6ayDgPr1zu6vfrdToAQ9W2x9LX1wdgaGn5RAv8xsbGQiwWw2g02u1dFgqF2L9/P86cORMUdQM8uY62pJsqmBPiOZdnujds2ODScdu3b/c4GEJCRXR0NNatWxfoMEICjTX/0TX0Hz6ONR9j5qOJjLMnPbp1Oh0kGNrTPRZb0u2N5eAMwyApKQnNzc3o7OzEtGnTAAwl3dHR0ejr60Nvby/kcvmEXyuQPLmOlHQTMnEuJ922IgqEEEIIIYS4ypOkOzomBlKh2a6o2XBmsxlqtRrAxIqoDZecnIzm5mb09PRAq9VyfbljY2PR19eHnp4eZGVleeW1+MS2p/vKlSswmUyjiigTQsbnctL91ltv+TIOQkKKSqXC4cOHce2119INLR+jseY/uob+w8ex5mPMfDSRcfYk6c7Ly0OcZex92v39/WBZFnFxcS7vEx9PeHg4oqKioFarcf78ea5oWGxsLOrq6riK6nzmyXWMiopCREQEBgYGUFdXh8LCQh9HSUjwoT3dhASA1WqFVquF1WoNdChBj8aa/+ga+g8fx5qPMfPRRMbZVkjNnaR7PMP3c3uTrWf32bNnwbIsgKGkG0BQJN2eXseCggIAVEyNEE9R0k0IIYQQQnzG3Zluk8kEi9ns9Bhv7uceLiEhAQKBAN3d3WhrawPwzfLq4QXWQo0t6aZ93YR4hpJuQgghhBDiM8Orl7uiqakJhw4fRn19vcPn9Xo9dDodGIZBdna2t8IEAIhEIiQkJAAYmu0Gvpnpti1pD0W2JeU0002IZ9zu000IIYQQQogrtFot+vv7Abg+021rFyaRSBw+b5vlVigUYx4zEUlJSQCAiooK3HjjjYiJicF//Md/ICYmJmRb09FMt+ssFgsOHjyI9vZ2pKSkYNGiRRAKhYEOiwSYSzPds2bN4j7gnn/+eQwODvo0KEKCXWRkJBYvXsxVRiW+Q2PNf3QN/YePY83HmPnI03G2zXJHRUUhKirKpe8ZvJp0y2Qyh8/bfieNiYlxKxZXxcTEQKFQQK/Xo6amBgKBAAkJCRCJfD9XxbIs+vv70dXV5ZOZdU+vo22mu6amJmRn+12xfft2ZGdnY+nSpfjWt76FpUuXIjs7m1oqE9eS7urqagwMDAAAnnvuOWi1Wp8GRUiwCwsLQ3JyMrXd8AMaa/6ja+g/fBxrPsbMR56Os7v7uVmW5Wa6HSXdLMv6POkGgJkzZwL4Zom5PyiVShw7dgznzp1DdXU1zp07h2PHjkGpVHrtNTy9jlOmTAEwdMPDm/EEk+3bt2PTpk1c4UCb1tZWbNq0iRLvEOfSLbvS0lJ85zvfwXXXXQeWZfHSSy+NeYfsF7/4hVcDJCQY6XQ61NXVIS8vb8w7+cQ7aKz5j66h//BxrPkYMx95Os7uVi43GAywsiwEDONw6bhGo4HZbIZIJPLp6obS0lIcOHAAV65cgUqlglKpREVFBVJTUzF37lyvv55SqURlZeWoxw0GAyorKzFt2jTExYRP+HU8vY7h4eHIyspCY2MjLl68yO17J0MsFgu2bNnicBUAy7JgGAZPPvkk1q1bR0vNQ5RLM91///vfERcXhx07doBhGOzcuRMff/zxqD+ffPKJj8MlJDjo9XpUVVVBr9cHOpSgR2PNf6F+DauUmnH/eAsfx5qPMfORp+Ps7ky3bnBollsqkzncPz18ltuX+6tjYmK4Im3nzp1DT08Pzp49i7q6Oq+/ltVqRW1trdNjamtrvbKseyLvF9rXPbaDBw+OmuEejmVZNDc34+DBg36MikwmLs10FxQU4IMPPgAACAQC7N27F4mJiT4NjBBCCCGE8Ju7lcsHdUN1g8LH2c9tqyjuS6WlpWhoaMDZs2exevVqAL7p1d3U1ASDweD0GIPBAJVKhejoaK+/vqsKCwvxxRdfUNLtQHt7u1ePI8HH7ZZhVquVEm5CCCGEEDIud2e6I8IjkJKSghgHSbXZbIZarQYAvySfRUVFEIvF6Ovr45Li3t5erxcS02hcW61iNBq9+rruorZhY0tJSfHqcST4eFSGsa6uDq+88gqqq6sBAMXFxdiyZQvy8vK8GhwhhBBCCOEvd5Pu6JhoRMdED31hse+WY6vmHR4eDqlU6s0wHRKLxZg2bRrOnDmD2tpaCAQCWCwWr884y+Vyl+MJJFpePrZFixYhMTERXV1dDp9nGAbp6elYtGiRnyMjk4XbM927du1CcXExjh8/jhkzZmDGjBk4duwYpk2bht27d/siRkKCTlhYGDIzM6narh/QWPMfXUP/4eNY8zFmPvJknG37WAHXk25n/FG1fKTS0lIAQFVVFZdoe3uJeWZm5rjtyMRhYS63XHNmIu8X20x3fX39uMvhQ01DQwN0V6vuj+WVV16hImohzO2Z7p/85Cf4/ve/j9/+9rejHv/xj3+MG264wWvBERKsIiMjMX/+/ECHERJorPmPrqH/8HGs+RgzH3kyzmq1mmsz60rSzbIsBgYGIJPJHCYngUi6MzIyEBcXh56eHi5R7e3tRW5urtdeo7a2Fmaz2ekxJrMZFRUVKC4unlC/8Im8X1JSUhAZGQmtVou6ujoUFxd7HEcwUSqVWL16NTQaDXJycmA0GtHa2so9HxMTg7/+9a/YsGFDAKMkgeb2THd1dTUeeOCBUY/ff//9qKqq8kpQhAQ7i8UCjUYDi8US6FCCHo01/9E19B8+jjUfY+YjT8bZNssdGxuL8PDx213pdTqcOnUKR48cAUbsm9br9dDpdGAYxq/FxBiG4Xp2i9NykVgyG12QeLWTgF6vBwMgWqEY1SZNIpEgIyMDAoZBX18fTp06hYGBAY9/nom8XxiGoX3dI+j1eqxfvx6XL19GZmYmDh8+jMbGRuzbtw+33HILAODWW2+lhJu4n3QnJCTg7Nmzox4/e/YsFVgjxEVqtRo7d+7kCsIQ36Gx5j+6hv7Dx7HmY8x85Mk4u1u5XKcbamMllcmAEe3AbLPcCoXC70t0Z86cCYZhYDAaMWf2bK6VmLfMmDEDpaVlmDFjBubNm4eZM2eiqKgIM2fOxLx585Cbm4uysjJIpVLo9XqcOXMG3d3dHr3WRN8vtK/7G1arFffccw8OHz4MhUKBnTt3IiUlBUKhEEuWLMF9990HADh16lRgAyWTgtvrUx566CE8/PDDuHLlChYuXAgAOHz4MH73u9/hqaee8nqAhBBCCCGEf9zdz+2sXVgglpbbREVFIS8vD2oAXd3dyImMnPA5+/r6EBYWhsir54pSfLNf29FMfkRkJGbNmoXq6mr09fWhqqoKmZmZyM7O9mm/8pFopvsbP/nJT/DPf/4TYWFh+Pjjj0ctt58zZw4AoKKiAjqdDrIx2uCR0OB20v3zn/8ccrkcL7/8Mp555hkAQGpqKn75y1/iiSee8HqAhBBCCCGEf9xNum2FqKQjkhOWZQOadANDBdUO1NSjs6NjwomuVqvF//t//w8A8O1vf9vlnyksLAzTp09HfX09mpub0dTUhPDwcCQlJXkci7topnvIH//4R/z3f/83AOCtt97C0qVLRx2Tnp6OpKQkdHZ24uzZs1iwYIG/wySTiNvLyxmGwfe//320tLRApVJBpVKhpaUFW7ZscfsD6MCBA1i7di1SU1PBMAw++eQTu+dZlsUvfvELpKSkQCaTYcWKFbh8+bLdMb29vdi8eTOioqIQHR2NBx54gCvaQQghhBBCAqOlpQWA+0n3yP3fGo0GZrMZIpGImxn2t4KCAoSJRDAYjTh16hQMer1H5zGbzfjHP/6Bvr4+sCzrdlE0hmGQm5uLoqIiJCYm+jXhBr6Z6a6pqfF6v3K+uHTpEh5//HEAwK9//Wts3rzZ4XEMw2Du3LkAgBMnTvgtPjI5uZ10DyeXy13uLejIwMAAZs6ciddff93h8y+++CJee+01vPHGGzh27BgiIiKwcuVK6Id90G3evBmVlZXYvXs3duzYgQMHDuDhhx/2OCZCCCGEEDJxns50y6T2M93DZ7n9uZR6OJFIhISrtYsGBgYwOOi8PZQjVqsVlZWV6OzsREREBL797W97/Ht0YmIiioqK7M7t7VZmjuTn54NhGKhUqjF7Ugez9vZ2bNu2DVarFQ8++CB++tOfOj3etsT85MmT/giPTGKe9xzwgtWrV2P16tUOn2NZFq+88gr+8z//E+vWrQMAvPPOO0hKSsInn3yCO++8E9XV1SgvL8eJEye4f9R/+MMfcNNNN+Gll15Camqq334WQtwRExOD22+/PdBhhAQaa/6ja+g/fBxrPsbMR56MsztJt9Vq5SZVZOGOk+7Y2Fi3Xt/bUpJT0NbWBgDQDmgRE+v6UneWZVFdXY3+/n5IJBLcfffdXv15Ll26hM7OTrS1tWHp0qUQCBzPq030/SKTyZCdnY36+nrU1NT4faY9kPr7+/Huu+/CZDJh5cqV+OMf/zjuTSCa6SY2E5rp9qX6+np0dHRgxYoV3GMKhQLz5s3D0aNHAQBHjx5FdHQ0l3ADwIoVKyAQCHDs2LExz20wGKBWq7k/Go1nLR4IIYQQQshoLMu6Vb2cZVnk5uYiNTUV4qv9sIGh5di2Stv+bBXmSGRkxDe9unt63PreS5cuQalUQiAQ4M4770RycrLX4mJZlovr0KFDeP/99+1WhXpbKBZT0+v1ePfddzEwMIDk5GSugNp4bDnKxYsXqcNCiJu0SXdHRwcAjLqDlpSUxD3X0dExqk2ZSCRCbGwsd4wjL7zwAhQKBfdnZLVBQnxNrVZj79699AHsBzTW/EfX0H/4ONZ8jJmP3B3n3t5ebrm4K0m3UChERkYGpkyZYtcurL+/HyzLIjw8HFKp1LPgvWVYj3CNRoOuri4uPmdMJhPUajUYhkFRUZHXW44xDIO8vDwUFRVBJBKhtrYW//d//+ewrZg33i+hVkzNYrHgww8/hFKpRFRUFL71rW+5vC0gMTERmZmZYFkWp0+f9nGkZDJza3m5yWTCqlWr8MYbbwx9KPLUM888Y9ferLW1lRJv4lcWiwU9PT2wWCyBDiXo0VjzH11D/+HjWPMxZj5yd5xts9wJCQkTSpYDXbV8JIlEAgCwWK2orq7mHsvPz0d8fLzD7wkLC0NpaSlUKtWYx3hDYmIi7r//fnz44Yfo7e3FX//6V6xfv57b+221WtHc3Iyenh40NzejqKhozGXozoTSTDfLsvjXv/6FhoYGSCQSbN682e19+HPmzEFTUxNOnjyJJUuW+CbQEHPw4EHU1NRAqVRCJBIhIyMDK1assHt/mc1m7Nq1C5WVlTCbzcjPz8dNN90UsGKMbr3TwsLCcP78eV/FYse27Kazs9Pu8c7OTu655OTkUUUczGYzent7nS7bkUgkiIqK4v5MpBgcIYQQQgix527lcq1Wi4GBAVitVrvHJ1PSrVQquZ9rOIPBgMrKSiiVylGP24SFhfk04bZJSUnBQw89hOzsbBiNRuzYsQMGgwHV1dV49dVXsWPHDgDAjh078Oqrr3I3DtwRSjPd+/btw4ULFyAQCHDHHXeMWmHrCtrX7X2NjY2YO3cuHnjgAXz729+G1WrFP/7xDxiNRu6Y8vJyXLp0Cbfddhvuu+8+aDQabN26NWAxu3176+6778abb77pi1js5OTkIDk5GXv37uUeU6vVOHbsGNfnbsGCBejv78epU6e4Y7788ktYrVbMmzfP5zESQgghhJDR3K1cXldXh5MnT9otidbr9dDpdGCGLesOFKvVitraWqfH1NbWckvNlUoljh07hvb2dn+EZ8dWGX3+/Pm47bbbcOXKFWzdunXUknK1Wo2tW7eOulkwHttMd0NDg0/3jgfa6dOncfDgQQDALbfcgpycHI/OQ0m39919990oLS1FYmIikpOTsW7dOqhUKu79ptfrcebMGaxcuRI5OTlITU3FunXr0Nzc7PDGmT+4Xb3cbDbjb3/7G/bs2YPZs2cjIiLC7vnf//73Lp9Lq9XafYDV19fj7NmziI2NRWZmJp588kn8+te/xpQpU5CTk4Of//znSE1Nxfr16wEARUVFWLVqFR566CG88cYbMJlMeOyxx3DnnXdS5XJCCCGEkABxu13Y4CCAoerYNrZZboVCAaFQ6OUI3dPU1GQ3c+2IwWCASqUCAFRXV4NlWajVaqSkpPgjRDsCgQArV66E1WrFq6++6vTY2tpaxMXFudyOLSkpCVFRUVCr1aitrUVJSYk3Qp5Uamtr8dlnnwEAlixZgpkzZ3p8rtmzZwMYynN6enoQFxfnlRiDkUajsbs5JJFIuC0dztjem7bPj/b2dlitVuTm5nLHxMfHQ6FQoLm52aU6E97m9kx3RUUFZs2aBblcjkuXLuHMmTPcn7Nnz7p1rpMnT6KsrAxlZWUAgKeeegplZWX4xS9+AQD40Y9+hMcffxwPP/ww5s6dC61Wi/Lycru9Qe+++y4KCwuxfPly3HTTTbjuuuvwl7/8xd0fixC/Cg8Px7x58xAeHh7oUIIejTX/0TX0Hz6ONR9j5iN3x9mdyuUWiwWGq8tCHSXdk2FpuaudbtRqNSoqKmC1WhEfH4+pU6f6ODLnmpqa7JIYo9GI1tZWu2W4w28WuIJhGG62OxiXmHd0dOCf//wnrFYrSktLcf3110/ofNHR0VwtLOrX7VxxcbFdsesXXnhh3O9hWRbl5eXIyMjglv9rtVoIhcJR9SQiIiKg1Wp9Evt43J7p3rdvn9defMmSJU4rPjIMg+effx7PP//8mMfExsbivffe81pMhPiDRCJBVlZWoMMICTTW/EfX0H/4ONZ8jJmP3B1nd2a6bVXOw0Qirg0Ty7KTKul2tf5PY2MjrFYroqOjUVRU5PLssa+MvFlgtVodVi4fnoS7oqCgAMePHw+6YmpNTU147733YDQakZubi5tvvtkr550zZw4uX76MkydPYuXKlV45ZzCqqqpCWloa97Urs9yfffYZurq6cP/99/sytAnzuGVYbW0tdu3axX1QjtcugRDyDb1ej8uXLwf1XqjJgsaa/+ga+g8fx5qPMfORu+PsatLNsiy3pzhMLOZ+n9SoNTCbzRCJRAGrNjxcZmamSwmA1WoFwzBISEgIeMINYNQ2UKFQiJiYmFHL9V3pOT1cMM509/f346abboJGo0FiYiJuv/12r21roH3drpHL5XbFrsd7z33++ee4fPky7r33XkRFRXGPR0ZGwmKxjPq8GhgY4Ef1cgDo6enB8uXLMXXqVNx0003chvUHHngAP/jBD7weICHBSKfT4cyZM9xNK+I7NNb8R9fQf/g41nyMmY/cGWeWZV2qXm4rNtbY2AgAGBwcxLFjx6BUKtHb1wtgaJZ7MiSvAoEA+fn54x7HMAxYlsXly5dx4sQJdHV1TaqJqbCwMCQnJ7udZI9kq2DO55lui8WC/fv34/3338fu3buxYcMGVFZWQi6XY/PmzS7dZHGVLemm5eXewbIsPv/8c9TU1OCee+4ZtRomJSUFAoEAV65c4R5TKpVQqVQu15nwNreXl3//+99HWFgYmpqauL5/AHDHHXfgqaeewssvv+zVAAkhhBBCCH90d3fDaDSCYRi7paLDKZVKVFZWjnrc1n4rXCaDDEPbCCeL+Ph4ZGRkoKW5GcPTaAZAWno6JBIJpiik6O3tRVNTE3Q6Haqrq9HU1ITs7Gy/tAyzYVkWjY2NOHTokEvHm0wmt84/fKabZdlJcWPEHdu3b8eWLVtGVbKWSqXYvHmz3aypN5SVlUEgEKC1tRXt7e0BKa4XTD7//HNcuHABd955JyQSCbdPWyKRICwsDFKpFGVlZfjiiy8gk8kgkUiwc+dOpKenB6SIGuBB0v3FF19g165dowKeMmUKd6eSEEIIIYSEJtvS8rFmVF1pvzWo00EGBLxV2HBKpZL72YZjMdSXfNq0aQgPD0d4eDhSUlLQ2tqK5uZmDAwMoLKyEpGRkcjJyUFiYqLPktSWlhZUVFSgqqrK5eJvACAWi916nfz8fAgEAmg0GnR0dPAqidy+fTs2bdrkcAWCXq9HX18fkpKSvPqaERERKC4uRkVFBU6cOIFbbrnFq+cPNbYVA2+//bbd4+vWrUNpaSkAYNWqVdi1axe2bt0Ki8WCvLw8rFmzxt+hctxOugcGBhxWruzt7fXqMgxCCCGEEMI/41Uud6X9FjA0azWy+nCguNqne+qc6WAYBkKhEJmZmUhNTUVLSwtaWlqg1Wpx4cIFHDlyBMuWLfO47/NIWq0W3d3d6OrqwpkzZ7jHpVIppl63HD09PdxMtkgkgiA6GrGSKJjNZgCARGSBQqFw6zUlEglycnJQV1eHmpoa3iTdFosFW7ZsGXPJP8MwKC8vR0FBgddvjMyZMwcVFRU4efIkJd0T9Oyzz457jEgkwpo1awKaaA/n9p7uRYsW4Z133uG+ZhgGVqsVL774IpYuXerV4AgJViKRCElJSRCJ3L7vRdxEY81/dA39h49jzceY+cidcR6viJqrM7CTqQ2cu326bUQiEbKzszFv3jxkZGRAIBCgpaUF77zzDt5++200NTXZHc+yLPr7+9HV1YX+/v4xk8PBwUE0NjbixIkTOHXqFJqamqDX6xEWFobp06fjrrvuwtNPP42CggK7tmWs1Qqj0QjWauUey8/P9yjBtC0x59O+7oMHD45aUj4cy7JQqVSjros3UDG10Ob2/1Avvvgili9fjpMnT8JoNOJHP/oRKisr0dvbi8OHD/siRkKCjlwun3DfR+IaGmv+o2voP3wcaz7GzEfujPN4Sber7bcm09JyV28UjNV6KywsDLm5uUhPT0dERAROnTqFhoYGvPXWW8jPz0f+wqUwGAyora21S+4lEgny8/MRHx8PvV7PzWgP7zUsEAgQFxeHhIQE3HrrraOW9MfHx2PatGncuVVXW4Zx547x7OZGQUEBPvvsM15VMLcVgB6PO0vzXTVnzhwAQ0uj+bgPnkyM20l3SUkJLl26hP/93/+FXC6HVqvFhg0b8Oijj/JmaQkhgWa1WmGxWCAUCiEQeNy5j7iAxpr/6Br6Dx/Hmo8x85E74zxe5fLMzExIKi6PO3OcmprqWbA+4OqNgvH2RovFYqxevRoLFy7EgQMHcPbsWdTW1kItdby8266wnLaHe5xhGMTGxiIxMRFxcXFca6uxqpLHx8cjLi4OfX19qKyogJVlUVJSgoGBAXR3dyMhIcGln284Ps50x8XFuXScq9fbHTNnzkRYWBiUSiUaGxuRnZ3t9dcgk5dH/zspFAr87Gc/w9atW/H555/j17/+NSXchLhBpVLh448/HrUMjXgfjTX/0TX0Hz6ONR9j5iN3xnm8mW5X2m9FhIdPqi0DrvTplkgkLu+NVigUWLt2LR599FHMmDFj3OMHr7Zqi4mJwdSpU7FgwQKUlJQgMTHR5V7SDMMgXCZDXFwcRCIRWlpaUFNTg4aGBpe+fyRb2zC+zHT39PTgV7/6ldNjGIaBQqFAZmam119fIpFw15pah4Uej5Luvr4+vPTSS3jggQfwwAMP4OWXX0Zvb6+3YyOEEEIIITwzXtINDM28Dm89a2ObRU9ITPRNcB5y5UaBJ3ujY2NjUVZW5tKx06ZNw4wZM5CSkjLhPtsAAJaFSCjE4OCgR7/H22a6GxsbXerfHkiXL1/GggULcOjQIchkMgAYda1sX69atcpnS79tS8xpX3focTvpPnDgALKzs/Haa6+hr68PfX19eO2115CTk4MDBw74IkZCCCHEbzo7O8f9Q4JTlVIz7h/inMViQWtrK4Cxq5fbSMRDM8cikQhFRUWYMWMGl+zExkye/tw2tr3RI2e8JRIJpk2b5nEfblf3D1uHFT/zBrVajeSrK1WdFRcbS0JCAqKjo8GyLC5fvuzV2Lzp4MGDmD9/Pi5fvoysrCycOHECH3300age8unp6di2bRt3M8EXqJha6HJ73c6jjz6KO+64A3/605+45SwWiwX/8R//gUcffRQXLlzwepCEEEIIIWTy6+zshNlshkAgGHfroVozVNArOjoaiYmJUKvUsFgsEIlEiJRHAtbJN3tq2xutUqlgNBohFouhUCgmNDPqrf3i7tLp9UhISEBz02X09fVhYGAAERERLn8/wzAoLCzE119/jZqaGpeWyfvbe++9h+985zswGo2YO3cu/v3vfyM5ORnTpk3DunXrcPDgQbS3tyMlJQWLFi2CUCj06Y1V20z3qVOnYLVaqQ5FCHH7StfW1uIHP/iB3f4RoVCIp556atz+hYQQQgghJHjZZkxTU1PH3ZOtvlpFOyoqCgDQ2ze0xDkmJmZSV3ZmGIa7URAdHT3hWL29X9wV4VeXWOv1em6G3rZCwR22fd2TsZjagQMHsHnzZhiNRtx6663Yv38/kpOTueeFQiGWLFmCu+66C0uWLHF5b/xETJs2DVKpFGq1elKvDiDe53bSPWvWLFRXV496vLq6GjNnzvRKUIQEO4VCgVtuucWr/4ESx2is+Y+uof/wcaz5GDMfuTrOruznBgCw7DdJt3wo6e7v6wMwlHSHEl/tF3dEIpViakEBIq/OrqtUKm6ZdWdnJ0wmk1vnsy3FnkzF1CwWCz755BPs27cPAPD0009j27Ztk6Lvu0gk4vbwUzG10OLS8vLz589zf3/iiSewZcsW1NbWYv78+QCAr7/+Gq+//jp++9vf+iZKQoKMQCCAVCoNdBghgcaa/+ga+g8fx5qPMfORq+PsctINoKy0FGqNBnJ5JMxmM5eEh1rSDYzupW0zvE83LIMTfh2GYSASiRAdHY22tjaoVSooctMgl8shEolgNpvdKtI22dqG6XQ6bN26FQ0NDRAIBPjTn/6E733ve4EOy87cuXNx9OhRnDhxAps3bw50OMRPXEq6S0tLwTAMWJblHvvRj3406rhvfetbuOOOO7wXHSFBSqvV4uzZsygtLUVkZGSgwwlqNNb8R9fQf/g41nyMmY9cHWeXk26GgVQmg/TqMudepRIshpY9h+pNFF/sFx/JaDSio6MDcbFDheq0AwMwm82YOXOmR8urhy8vZ1k2oNsC+vr68N5770GpVEIikeC2227DwoULxzzearWiqakJGo0GcrkcmZmZftljbSumRjPdocWlpLu+vt7XcRASUkwmE9ra2jBt2rRAhxL0aKz5j66h//BxrPkYMx+5Os62pHu8yuUj9V1dWh4dgrPcw9n2i/uK1WKBVqNBYkICwmUyDOp0UKlUiIuL8+h8eXl5EAqF0Gq1aGtrG1UR3F9aWlrw/vvvY3BwEAqFAt/61reQ6KTtXHV1NcrLy7nVFcBQbYFVq1YhNtZ3lfM7OztRVlaGRx55BGFhYWhvbx+V6CclJfns9UnguJR0Z2Vl+ToOQgghhBDCc67OdF+5cgVhYWFITEyETqeDsrsbQGguLQ+UKIViVNJtNBqhVCqRmprq0jnEYjFyc3Nx+fJl1NTUBCTprqysxCeffAKz2YzU1FTcddddTldjVFdXY+vWraMeV6vV2Lp1KzZt2uRx+zdXxMXFQSKRwGAwQKlUOr05QIKH2y3DAKCtrQ2HDh1CV1fXqJ6BTzzxhFcCI4QQQggh/GKrXu4s6TabzWhpaQHLsmhpaYHRaOSeq71a0dmXSQ8ZolAo0NHRAZVKBWCoANnx48dhsVgQGRnJVZUfT2FhIZd0L1++3Jchj3Lo0CHs3buXi2PDhg1O96RbrVaUl5c7PWdtbS3i4uJ8tlSeYRikpKSgoaEBbW1tlHSHCLeT7r///e/47ne/C7FYPOofJMMwlHQTQgghhIQgs9mMtrY2AM6T7vb2dq5O0PCEGwAMRiMqKysxbdo0xMUEvtp0MIu+Woleo9HAarVCKBQiISEBHR0daG1tdTnpLigowKeffurXYmomkwn//ve/cebMGQDAggULcMMNN4ybKDc1NdktKXfEYDBApVL5dJl/WloaGhoa0NraitLSUp+9Dpk83K4W8POf/xy/+MUvoFKp0NDQgPr6eu7PlStXfBEjIUFHJpNh5syZkF0tIEN8h8aa/+gafoO1WtHQ0ICKigo0NDSAHbHabKL4ONZ8jJmPXBnn9vZ2WK1WhIWFOd2XaluC7kxtba1dAV/iHaKwMCQlJ0MUFgapVAqJWAx2WPs22/Lw7u5uuyrqzkydOhUAsG/fPuzfvx8Wi8Vr8VosFuzfvx/vv/8+d+7+/n6sXr0aZ86cAcMwuOmmm3DjjTe6NDOt0Whcet2RN4O8zbZ833aTigQ/t2e6BwcHceedd/qluh8hwUoqlXIVP4lv0VjzH13DITXV1djpoPDP6lWrULzoGq+8Bh/Hmo8x85Er42xLptPS0pz+nnjp0iVA7nz5uD9mG0ORSCSyK5qmiI6GRdfPjXVkZCSio6PR39+PtrY25OTkOD3f9u3b8bOf/QwAUFFRgaVLlyI9PR2vvvoqNmzYMKFYt2/fji1btnBbFgAgOTkZQqEQra2tKCkpwaZNmzBlyhSXzym/2p98PGKx2O143WFLujs7O2GxWDyqHE/4xe3M+YEHHsA///lPX8RCSMgwGo1obm72+Z1UQmMdDOgaDiXcW7duHbUsUnO18M/27du98jp8HGs+xsxHroyzq5XLOzs7XX5N4l0WiwVqlZqbjVZcXWJu29cNfDPb3dbWNqp203Dbt2/Hpk2b0H21CJ5Na2srNm3aNKHPJdu5hyfcALil7zExMbj//vvdSriBoW0P400cSiQSblx8JTo6GuHh4bBYLOjq6vLpa5HJwe2Z7hdeeAE333wzysvLMX369FHFCn7/+997LThCgtXAwACOHj2KG264wed3U0MdjTX/8eUaVinHX7ZYHO/aLMtwrNWKneXlcLTQlgXAAHjyySexbt26Cc+W8GWsh+NjzHzkyji7UrlcrVZDr9fDld3CdD29z2Q0oqWlGbm5uRDKZFBERaEXQ9fF1mc7Li4OUqkUer0enZ2dSElJGXUei8WCnacqcNujTzl8HQZA+akKLFy4cNxl3yO3IlgsFmzZssXp9gKZTOZRATKhUIjly5dj9+7dYx6Tn5/vl37jqampqK2tRWtrq8MxJsHFo6R7165d3BKjkYXUCCGEEOI9jeMU/mExlOwcPHgQS5Ys8VtchIzkSuVypVIJoVAIhmGcJlX+mG0kQEREBEQiEcxmM7RaLeRyORiGQXp6Oq5cuQKTyeTw+w4ePDju55JKrca7777rtH0XABw5csTu646OjlEz3CO1tbWhqanJ5bbGZrMZItFQ2rNw4ULExMQEpE/3cLakm/Z1hwa3k+6XX34Zf/vb33Dffff5IBxCCCGEDKfVal06rr293ceREOKcKzPdubm5+MlPfoL9VXW4fLU9mCP+mm0MeQwDhUKBnp4eqFQqbs9zcnIyEhMTx2y/5ernTV1d3bjHvPPOO67HO4yrRdGam5u5/tu2JL2oqAgFBQVoamqCRqOBXC5HZmYmWlpa0N3djYSEBI9icgcVUwstbifdEokE1157rS9iIYQQQsgI480S2dDyRBJoriTdwFAxr9TUVAgEglFtpiQSCfLz84f6dFsGfRYr+cbwpNu2H18oFDrdruLq582cOXPGnTnOzc21+7qurg5vvPHGuOd2pShaY2Mj3n33XZhMJhw5csRuZlwgECA7O5v7uqGhAW+//TamT5+OqKgoSCSScc8/Ebaku6urCyaTyWl/ccJ/bifdW7ZswR/+8Ae89tprvoiHkJAgFAoRHR1N1Sr9gMaa/0L9GmZlZiIqKgoatdrhvm4GQ0nOokWLJvxafBxrPsbMR66Ms6tJt41tua9UKkVOTg7EYjEUCgXNcPsQIxBAIpWCGVZMzFExteE0Gg0kEoldJflFixbh3d0HnH4uRUVF4aabbhr3eq5fv97ua4vFgh07dqC1tdXhFgTb8vfMzEyn5+3r68NHH30Es9mM3NxcbNq0yenxmZmZSE9Ph9lsxuXLl1FSUuL0+ImSy+WQy+XQaDTo6Ohw+X1D+Mnt6uXHjx/H22+/jdzcXKxduxYbNmyw+0MIGV9UVBRuvPFGREW5UkqGTASNNf+F+jVkBAKsXrXK4S+2Nq+88opXkk4+jjUfY+aj8cbZaDRyVcnHql7e2tqKN954A3v27AHwTZIXExODxMREREdHU8LtYxKJBHl5eXazuHK5HAKBACaTCYOD9qsL6uvrcfr0aRw+fNjucaFQiNWrVgEYSrCHs329atUqj66nUCjEq6++OnSuEd9v+/qVV15xeu7e3l5UVFTAbDYjPz8fd91117gzyQKBALfccgsEAgF6enpGVWX3BVpiHjrcTrqjo6OxYcMGXH/99YiPj4dCobD7QwghZPKoUmrG/UMmvylTpkAyRiXnG264gW56k4CzzUpKJJIx98M2Nzejs7OTS2ZsRazo98fAYq7u6wZGz3bHxMQAAM6dOwedTmf3XGFREW6//XbIR9yIiYqKwu23347CoiKPY9qwYQO2bdvGtS+zSU9Px7Zt25x+5vX09KCyshJWqxUFBQW44447uFUV40lISOBm0C9fvjxmITlvsf18ra2tPn0dEnhuLy9/6623fBEHISGlr68Pe/fuxfLly7n/0Ihv0FjzH11DoLqmBgajEXK5HLfeeisGBgZw5vRpXKmvx5X6eq+9Dh/Hmo8x89F442yrNp2enj7mDKTtmLS0NFgtFq4QloJWKfiNXqdDfX09cnJyIJXJuMcVCgX6+vqgUqns9mtHR0cjMjISJpMJZ86cwcKFC+3OV3i1IFljUxO0Wi0iIyORlZlpt3zdUxs2bMC6detw8OBBtLe3IyUlBYsWLRp3VU9nZyesVisSEhKwePFit1cBZWZmoru7GwMDA6irq0NhYeFEfgynaKY7dEz8HUEI8YjVag10CCGDxpr/Qv0anjx5EgAwe/Zs5OTkoKSkBDffvBYCgQC1tbU4cOCA116Lj2PNx5j5yNk4u7Kfe3hLMY1GC5ZlIRaLIZVKvRsoccrRPmnbtoH+/v5Rz9lmY48fP+7w3wBztSBZSUkJsrOzvZJw2wiFQixZsgR33XUXlixZ4lICXVhYiLy8PBQVFXm07YZhGBQUFIBhGHR2djptjTZRthscPT090Ov1PnsdEnhuvytycnKQm5s75h9CCCGEeE93dzcaGxshYBjMKpvFPR4TG4NZs4a+fuaZZ5z2PCbE18ZLujUaDVQqFRiGQWpqKlTqoWXMiqgogPZxB1xUVBQYhoHBYBiV/CUmJiI8PBwqlQo1NTUBitC54YmxQCBwuuLCFXK5HFlZWSgsLPRpvYjw8HCuQF1HR4fPXocEntvLy5988km7r23LTcrLy/HDH/7QW3ERQgghBMCpU6cAAFMLCiCPsm+Rs3jxYpw7exZHjhzBjh07sHbt2kCESMi4SbdtljsxMRESiYRLkqJoP/ekIBQKIZfLoVaroVar7VYfCAQCzJkzBwcOHMDXX3+N4uLiAEY6WkdHBy5evIi0tDTk5+d77bzD24v5UlpaGvr7+9Ha2mrXwowEF49ahjny+uuvc8vfCCGEEDJxg4ODOHf2LIChfrcjyeVyzJs3D+8C+NnPfoY1a9ZA4MWlnYS4ypZ0j1W5fPjzLMtCfbVgF1WdnzwUCgXUajX6+/uRmJho99ycOXNw6NAhqFQqDA4OIjw8PEBR2mtra8Ply5d9/jpmsxlGo9EnP3dqaioqKytpX3eQ89r/zKtXr8ZHH33krdMREtTkcjlWrlwJuVw+/sFkQmis+S+Ur+GHH34IvcGAmOjoMbdwXXvttYiOjsaFCxfw/vvvT+j1+DjWfIyZj8Yb5/FmuqVSKWJiYpCeno6enh6YzGYIBALIIyN9FjMZTXy1ZZh4WMswG2f9uuVyOb7zne/giSeemDQJd0tLC5dwp6ene3WWeziNRoOTJ09yFdG9jYqphQavJd3btm1DbGyst05HSFATiURQKBQut7AgnqOx5r9QvoZvvPEGAGD2nDlj7k+UymT40Y9+BAD4+c9/DqPR6PHr8XGs+RgzH403zsOLpDmyePFiPPHEE5g5cyaampoAAFFyuVeLbpHxCQQCSKRShytibKsOBgcHHbbKSk9P96gwmS8cOnQIdXV1AIaqjefl5fnstWQyGViWxeDgIPdv15tsxdT6+/tH9UknwcPtT7qysjLMmjWL+1NWVoaUlBT89Kc/xU9/+lNfxEhI0BkYGMCJEycwMDAQ6FCCHo01/4XqNTxz5gyOHz8OoUCA0tJSp8c+8cQTSE5ORn19Pf761796/Jp8HGs+xsxHzsZZr9dzvbedVS8HhipD22bFaT+3/5mMRrS1tcHk4OZcWFgYIiIiADie7baxWq3o6uryWYwjX6uhoQEXLlxAQ0MDrFYrDhw4gL179wIAsrOzkZOT49MYRCIRpkyZAgBoamry+meNRCJBfHw8AJrtDmZu3xZev3693dcCgQAJCQlYsmSJT/vYERJMjEYj6uvrkZ+fz/0HR3yDxpr/QvUa/vnPfwYAFBUXj/tzR0RE4Oc//zkeffRRPP/887j33ns9Gis+jjUfY+YjZ+Nsm+UODw932MNbr9dDIpFwqzWam5shTMmm/dwBYLFY0N/Xh9iYGIQ5eF6hUGBgYAAqlYpLBIfr6enBO++8A7PZjNX3PuzTGhLV1dUoLy+3q0weFRWF4uJiMAyDnJwcZGZm+uz1h4uPj0dCQgK6u7tx8eJFZGdne/VnT01NhVKppKQ7iLmddD/77LO+iIMQQgghV2k0Grz77rsAHBdQc+TBBx/ESy+9hPr6erz22mt45plnfBkiIZzh+7kdbYP4/PPPcenSJaxevRpTpkxBT08PElOyh9qFkUlFoVCgra1tzJnumJgYMAyDwcFBdHV1ITk52SdxVFdXY+vWraMeV6vV+Prrr7Fy5Uq/Jdw2+fn56Ovrg0ajwddff42FCxd67dypqak4f/48Jd1BjDbSEEIIIZPMu+++C61Wi8LCQmS5+IulWCzGr371KwDAiy++iL6+Pl+GSAjHlcrlBoMBkZGR3LHh4eEQhTmaayWBZCumptVqYbFYRj0vEAgwd+5cAEBrSwvAsl6PwWq1ory83OkxR48eBeuD13ZGLBZze8f37duH3t5er507LS0NANDa2ur3n4v4h8sz3QKBYNwm8wzDwGw2TzgoQgghk1+VUjPuMcXxVFHaXSzLcgXUvvvd7wLj/N8LAJ2dnQCA5cuX45e//CU6Ozuxc+dOLF++nDsmKSnJNwGTkOesiJpWq0V/fz+AocTi4MGDAKhV2GQlkUgglUqh1+uhVqsdbheYNWsWvvrqK2gHBtDfr0J0TLRXY2hqarJbUu6IWq2GSqVCdLR3X3s8ycnJ6OrqQlFRESQjKsC78n9i3BiPJyUlQSAQQKvVoq2tjUvCSfBwOen++OOPx3zu6NGjeO2113xSRp+QYCSRSFBYWDjqA5t4H401/4XaNTx27BjOnTsHqVSKe+65Bx1u/NfKMAyWL1+O9957D8eOHcM111zjVistPo41VOvE5gAAjZZJREFUH2PmI2fj7KxdmC0hT0hIgFQq5b5WUBG1gBCKRIiLj4fQSbX/6OhodHR0QKVSOUy6ZTLZUBV6PYvW1havJ92urtKZSKeGiSgpKeEqjjvCsixUKhWMRiPEYjEUCsW4E5dhYWFISEhAZ2cnTpw4QUl3EHI56V63bt2oxy5evIif/OQn+PTTT7F582Y8//zzXg2OkGAVHh6OGTNmBDqMkEBjzX+hdg1ts9x33HEHYmNj0eHC7MlwU6ZMQUZGBpqbm3HgwAGsWbPG5e/l41jzMWY+cjbOriTd6enpMJvNaG1tBQDazx0gYWFh4656USgUXNI9lnnz5qHpq6+h7OmBTqeDTCbzSnwnTpzAF1984dKxYrHYK6/pruEF1FiWhcVi4VrpKZVK1NbWwmAwcMdIJBLk5+c7LEw3XFpaGpd0jyxcTfjPoz3dbW1teOihhzB9+nSYzWacPXsWb7/9NrKysrwdHyFByWQyoaury2EfTOJdNNb8F0rXsLe3Fx9++CEA4Hvf+57H51mxYgUA4PTp027t7ebjWPMxZj5yNs6uJt3t7e2wWCwIDw/3WpJG3GO1WDAwMACrg/3aNral/2q1esxVrPHx8YiNjQUwVNHcUxaLBXq93u61zWbzuJXBo6KiAr5aQqPRYOvWrdi2bRtYloVSqURlZaVdwg0ABoMBlZWVUCqVTs+XmpoKADh58qTPYiaB41bSrVKp8OMf/xj5+fmorKzE3r178emnn6KkpMRX8RESlLRaLfbv3w+tVhvoUIIejTX/hdI1fOedd6DX6zFz5kzMmzfP4/NkZmZiypQpsFqt2Ldvn8vfx8ex5mPMfORsnMdKuq1WK1eN2bb6gjvOhVoFxPuMRiMaGxqcLs0ODw+HWCyG1WqFRjP2SpucnBzMKps1ZgE9Z7RaLS5fvoyjR4/i6NGj3ONTpkzBvffei40bNzr9/lWrVo27ZNvXdDodLl26hIsXL6KiogK1tbVOj6+trXVaJG140k3F1IKPy0n3iy++iNzcXOzYsQPvv/8+jhw5gkWLFvkyNkIIISRkDC+g9r3vfW/Cv1AuW7YMAHDhwgWu0Boh3jYwMMCtphiZfJnNZixYsAAFBQWIj493OiNOJhfbLLKzgmaRkZGQR8nBsiz6+/vR1dWF/v7+MRNGi8WCjo4OnDlzBqdOnUJbWxssFgsaGxu5YwQCAbKzs1FcXIzbb799VMG9qKgo3H777SgqKvLCTzkxiYmJuO666wAMtcUbOcM9ksFgcLpkPzExEUKhEL29vaivr/dqrCTwXN7T/ZOf/AQymQz5+fl4++238fbbbzs8bvv27V4LjhBCCAkVX331FS5evIjIyEhs3rx5wudLTk5GSUkJKioq8OWXX9K+Z+ITtuXjcrl81HJfsViMpUuXAhi6qWRLujMzM+FepQLibwqFAt3d3VCpVE5vkri6h7m+vh5tbW1clyOGYZCQkICUlBRMnTrV4bmLiopQUFCApqYmaDQayOVyZGZmjrv03J8WLVqE6upqdHd3w5UqBc5WGAiFQq7v+YkTJ5Cbm+ulKMlk4HLSfc899wR8GQchhBASrGyz3Js3b3ar4rgzS5cuRVVVFS5duoRDhw5xszI2I1vc6HUmCJIyUa81od0y9By1fSPOuDp73dfXh4GBAQiFQqSkpEDTr/NHeMRDthsoKpVqzJlr2x7mkWx7mIuLixEXGwFgqCaA2WyGTCZDSkoKkpOTEXa1T7uz/MI28z1ZiUQirF27Fn/7299cOn684m/Dl5jfcccdE46PTB4uJ91///vffRgGIaGFYRjIZDK6keUHNNb8FwrXsLOzk1sp9t3vftdr542NjUVZWRlOnTqFZ555BgcOHHA+jgwDUVgYr/bbhsK/j8lgrHF2lnTX1dUhOTkZERERaGpqAjCUVIictKsiPubiezwiIgJCoRBmsxkDAwOjnrdarePvYb58GQXXzATDMMjIyEBCQoLDFmR8l5GRgWuuuQYNg2MXpwOGVgCMV/zNlnSfOHHCa/GRyWHyrM8gJIRER0dj7dq1iI6ODnQoQY/Gmv9C4Rq+9dZbMJlMmDdvHsrKyrx67uuvvx4ikQiHDh3Czp07nR4rlUoxdepUSKVSr8bgS6Hw72MyGGucx0q6BwYG8I9//AMvvfQS9Ho97eeeJFx9jzMMYzfbPVJTU9O4e5iNJhP3vTKZLCgTbpvly5dzM/djyc/PH/fmoK0/96lTp2BxUmGe8A8l3YQQQkgAWa1W/OUvfwEwsTZhY5HL5Vwl9J/+9KdjtgAixBNjJdO2ftzx8fGQSqWUdPOQs6TbWVXz4ZztYQ4mYrEYM2fORGJiosPnoxWKcft0A0BcXBzCw8Oh1Wpx6dIlb4dJAoiSbkICoL+/H59++in6+/sDHUrQo7Hmv2C/hrt370Z9fT0UCgVuv/12n7zGtddeC4VCgXPnznF9wB3R6/W4dOmSXd/cyS7Y/31MFmONsy2ZHlm5fHh/bp1Oh+7ubgCUdAeaO+9xZ/u6Xa07Md4e5mCi0+nQ1dXl8Ll+lWrM54YTCASYNWsWAFpiHmwo6SYkAFiWhU6noz6MfkBjzX/Bfg1tBdTuvfdehIeH++Q1ZDIZfvjDHwIAfv7zn8NkMjk+kGVhNpkAHo11sP/7mCzGGmdbcj0ymR6edNv+HhcXh4iICD9ES8bkxntcLpdDIBDAaDRybeFsMjMzIZFInH6/K3uYg4XVakXt5ctOj7l8+bJLn1Nz5swBMFRMjQSPSZ10//KXvwTDMHZ/CgsLuef1ej0effRRxMXFITIyEhs3bqRepIQQwnNVSo3dn3rtNxW1bY/xVWdnp92fy5cvIy0tDY888ggeeughn/4ftmXLFiQmJqKurg5vvvmmz16HhBZHy8atViu3vDw9PZ0rokaz3PwiEAi4Ge3hvbRtz+Xn5zv9flf2MAeLpqYmGMZZSm82m11akTN37lwANNMdbCZ10g0A06ZNQ3t7O/fn0KFD3HPf//738emnn+Kf//wnvvrqK7S1tWHDhg0BjJYQQghx3ZkzZ2C1WpGVlYWEhASfvlZkZCR+/vOfAwCef/55DA4O+vT1SPBTq9VQq9UA7BPq7u5uGI1GiMViJCQk0H5uHrPNVNtunAwXHx+PadOmjZrxlkgkmDZtmkt7mIOFq3vce3p6xj3GlnSfPXt27FVJhHcmfc8GkUjENYofTqVS4c0338R7772HZcuWARiq/lpUVISvv/4a8+fP93eohBBCiMusVitOnz4N4JvlhL728MMP4+WXX0ZDQwP+8Ic/YO0D/+GX1yXByZZMx8TE2C0bty0nT0tLA8uy3Kw3Jd38Y0u6R85028THxyMuLg4qlYq70aJQKEJmhttmvMrlNr29veMek5eXB4VCAZVKhcrKSpSWlk4wOjIZTPqZ7suXLyM1NRW5ubnYvHkzd6ft1KlTMJlMWLFiBXdsYWEhMjMzcfToUafnNBgM3N1ZtVrt8t0pQrwlMjISS5YsQWRkZKBDCXo01vwnFouRlZ0ddAV5Ll++DLVajfDwcBQVFfnlNcViMZ5//nkAwG9/+1vodbpRz/NtrOk97h+OxnmsGewpU6Zg3bp1mDdvHjo6OmA2myGTyUJq5nOycvc9bkug+/r6xvx9mWEYREdHIzExEdHR0SGXcANwuZCjTqcbN/EWCATcjVhaYu5YY2Mj3n//fbz88st47rnnUFNTY/c8y7LYt28fXn75ZfzmN7/BO++849IqA1+a1En3vHnz8Pe//x3l5eX405/+hPr6eixatAgajQYdHR0Qi8Wj+kUmJSWho6PD6XlfeOEFKBQK7k9xcbEPfwpCRgsLC0NiYqLLd0aJ52is+U8gFCIiIgICoTDQoXiVrUhOWVkZhH782b71rW9h2rRp6O/vx5EjR+ye4+NY03vcPxyN81iVy6OiolBaWoqCggK7xDwUk7HJxt33uPDq8YDjJeZkyPnz58c9Ji4uDoBr40jF1JwzGo1ISkrCTTfd5PD5w4cP49ixY1izZg0efPBBiMVi/OMf/4DZbPZzpN+Y1En36tWrcdttt2HGjBlYuXIlPv/8c/T392Pr1q0TOu8zzzwDlUrF/amqqvJSxIS4ZnBwEOfPn6c9lX5AY81/JpMJnZ2dQbW3ra+vD3V1dQCA2bNn+/W1hUIh/uu//gsA8PWxY9BqtdxzfBxreo/7h6NxHqty+XBjJeYkMDx5j4+3xDzUdXV1ob29HQzDoKCgwGFV94KCAkydMgUCgYDLP5wJ1WJqGo3GbjWywWBweNyUKVOwbNkyh6vEWJbFsWPHsHjxYhQWFiIpKQnr16+HRqMZNSPuT5N+T/dw0dHRmDp1Kmpra3HDDTfAaDSiv7/fbra7s7PT4R7w4SQSid0bwlYEhBB/MRgMqKmpQUZGhs9aBJEhNNb8ZzGb0aNUQhEVFTSzmadPnwbLssjLy0NMTIzfX3/t2rVYsGABTCYTDh44gNVXZwscjbUrFdWTkpJ8Gq8zjt7jkzFmV6ruF8e71vs4EByNc3NzMx555BEsW7aMG3OtVguVSgWFQoHIyEju31FmZmbAYiff8OTz1FkxNQKcO3cOABAXG4vk5GQkJSVxe9wbGxsRLpNBoVBALJEgKSkJ7e3taG5udtpOzTbTfeHCBej1ekilUr/8LIE2cvXxs88+i1/+8pdunaO/vx9arRa5ubncY1KpFOnp6WhubkZJSYk3QnXbpJ7pHkmr1aKurg4pKSmYPXs2wsLCsHfvXu75ixcvoqmpCQsWLAhglIQQQsjYLBYLzpw5A8B/BdRGYhiGm+0+eerUqB68hLjCNos9PHlQKpWora1Fa2sr9Ho9jEYjBAIBUlNTAxUmmSDb9e3s7IRerw9wNJOL1WrllpYnXZ30G77Hfc6cOZhWUgKZTAbgm20WPT09GBgYGPO8mZmZSEhIgNls5pL6UFBVVWW3GvmZZ55x+xy21VvDizvavnY25r42qZPup59+Gl999RUaGhpw5MgR3HrrrRAKhbjrrrugUCjwwAMP4KmnnsK+fftw6tQpfOc738GCBQuocjkhhJBJq6amBgMDA5DL5Zg6dWrA4liyZAny8/JgtVqxf//+gMURalirFQ0NDaioqEBDQwNYqzXQIXnMlnRHRUVxj9lWD0ZFRXF/T0lJCZpVKqFILBa7tR85lFy5cgVarRYymQxxsbGjnh9Zx0Amk3HtIW3vH0cYhuGWmIfSvm65XI6oqCjuj6Ol+nw1qZeXt7S04K677kJPTw8SEhJw3XXX4euvv+b+sf7P//wPBAIBNm7cCIPBgJUrV+KPf/xjgKMmhBBCxmb7BWrWrFkQCAJ773vZ8uWoravDhfPnsWDBAm6/blt7O3Kys8EEOL5gU1NdjZ3l5Xbb2qKiorB61SoU+qmCvbewLOsw6bZVuJbL5WhvbwdArcKCQWZmJnp6etDY2BjQm4WTjW0WuqSkxOnnpU6ng0FvQFyUGBkZGejq6kJXVxeys7PHXDo+Z84cfP755yG3r3uibB0WbDe3bQYGBgK6FWpSJ90ffPCB0+elUilef/11vP76636KiBDvEIvFyMnJ4VVbHr6iseY/oVCI6JgYv1b49hWlUomGhgYwDINZs2YFOhykpKRg2rRpqKysxJtvvgmxWIzioiJUVVdDJBJh9apVuHZqVqDDdIov7/Ga6mps3boV7IjHNWo1tm7dittvvx3Fi64JSGyuGDnOfX193E0aW9I9MDAAs9nMVby23VygpHvy8PTzNDMzE2fOnKGZ7mH0ej1XmKu0tBT9YxzX29uLCxcuQCqVIm/OdERGRiI2Nha9vb1obm7GlClTHH5fqBZTm6jo6GhERkbiypUrXJ0vg8GAlpaWgG3pAib58nJCglVERATmzp07ar8J8T4aa/4LE4uRmpqKsEmeVLni9OnTAICpU6fazQ4GUtbVAldmsxmDg4M4eeoUBgcHuWQwkNVeXcGH97jFYsHO8vJRCTcA7rHy8nJYLBZ/huWWkeNsq1weHh4OkWhoDmf4LLfFYuH2VlIRtcnD08/TrKyhm29tbW286m7gS5WVlTCbzUhISEBKSsqYx0UrFBAKhdDr9VzVctt7oqOjA0aj0eH32RLE6upquy4TZKhlWEdHB9cmuq+vDx0dHVCpVGAYBvPmzcPBgwdx8eJFdHZ24uOPP4ZcLkdhYWHAYp7UM92EBCuz2YyBgQFERERwv6wQ3/D3WE/Gqsl8Z7VaYTIaESYWT3g5tisVpOMm9ApjM5tMOHv2LIDAFVAbibVacejwYe5roUCAiMhIDGi1sFitYDCUDBYUFEyaHssjr6Gjfx++uoaeOnjwoNNOKSwAlVqNU6dOccnNWAL1+THys9RRETVH+7llMhm33JMEnqefp9HR0ZDL5dBoNGhtbUV2drbvguQJ29LymTNnOv18FAiFSEhIQEdHBzo7O6FQKKBQKLj3SWtrK3JyckZ9X3JyMtLT09HS0oLTp09j8eLFPvtZ+KatrQ1vv/029/UXX3wBYOharF+/Htdeey1MJhM+/fRT6PV6ZGZm4u677w7o79z02z4hAaDRaLB7927ccMMNAWkXFEporPnPaDDgypUryM3NhfRqBVg+qqqqgk6nQ3R0NPLy8gIdDgCgsalp1P7iFStWYM+ePejr7x9KBlUqNDU1jZsMBspk//dhNpvx6aefAhj/poVtpngyGvlZ6mw/9/Cke7Ks6CBDPH2/MAyDrKwsVFRUoLGxMeSTbtvScIZhMGPGjHGPT0pMQkdHB7q7u5Gfnw+BQIDMzExUVFSgtbUVGRkZDhPCuXPnoqWlBSdPnqSke5js7Gw8++yzYz7PMAyWLl2KpUuX+jEq52h5OSGEEOIHtgJqs2fP9njWmGVZ9Pf3o6urC/39/WBZRwuWXefqksXJnAxOVk1NTfjFL36BrKws/P73v3fpe4YX/ZnsHM10l5WVobS0FAqFgku6nfUiJvxiWxJN+7q/meXOzc116X0bHa2ARCKB2WxGT08PACAuLg4RERGwWCxoa2tz+H22VVG0r5v/aKabEEII8bHOzk40t7QgQSBAaWmpR+ew9T82GAzcYxKJBPn5+YiLCffonK4u++VTMhhIZrMZn3/+Of7yl79g586dsF5tBxYXFwexWAyT0ehwXzcwNCPMp73Pjma6BQIBFAoFWJalme4gZPv32dzczP3bDlXDl5a7hGGQmJiIwYY+dHZ2cp2YMjMzUV1djZaWFqSnp49a8h+KbcOCFc10E0IIIT526uovTEVFRR7tb1UqlaisrLRLuIGhiqyVlZVQKpUexZWVmYmoqKgxFz4zGJqp5FMyGAjNzc345S9/iezsbKxbtw6fffYZrFYrli5divfffx+tra24df16AGMvMo+LjZ00++ZdYSuk5iipHhgYgMVigUgkmtQF7oh7EhMTIZVKYTKZuHZwoai/vx8qlQoSicStwlzJV+sxqNVq7qZFQkICN6a2omDDzZ49GwBQW1uLvr4+L0RPAoWSbkICJND9eUMJjTX/8SkZGcloMODc+fMAPCugxrIsamtrnR5TW1vr0VJzRiDA6lWrhv6OoYJeFqvVbjZ21apVk378vR0fa7WioaEBFRUVaGhoAOtgVs9isWDHjh1Yu3YtsrOz8dxzz6G1tRVxcXH4wQ9+gIsXL+LLL7/EnXfeOfTLeVERbr/9dshHJKnh4eFgANQ3NODAgQNe/Tm8bfhn6cjl5XV1daitrYVOp+MqNNMs9+Tk6fuFYRhaYo5vCqZOmzYNYWFhLn9feEQESkpKMH/+fO69xDAM11Kvubl51Od4bGwsVwOEZrv5jZaXExIAMTEx2LRpU6DDCAk01vwnlclQVFwc6DA8VlFRAaPRiLi4OI+KD6lUqlEz3CMZDAaoVCpER0e7fX5bMrizvBz9/f3Yvn0799zcuXMD2mLFFd7+91FTXY2d5eWjCsytXrUKhUVFaGlpwZtvvom//vWv3GwvAFx//fX47ne/iw0bNkAikTg8d2FREQoKCtDY1AStVovIyEhkZWbi9Jkz2LFjB/bt2weFQuH6klU/Gv5ZyrLsqJnujo4OmM1mJCUl0X7uSWyi75fMzExcunQJTU1NmD6lxIuR8YPFYkF3dzcAN5aWDxMXN7q3QnJyMhobG6HX69HV1cX1lraZM2cO6urqcPLkSdxwww2eBU4CjpJuQghxUSDbTVErMp5iWW52Ys7VZYLuGi/hthmr16srRiaDjY2NOHnyJCoqKjC46BqEh3u2Z5xvaqqrsXXr1lH7rjVqNT7cuhWpqakwttSBZVmsXbsWMpkMpaWlmD17NvfLdH9/v9P3IiMQjLr5Mnv2bPT39aH6wG78+9//RlRUlMMWQpOFUqmEXq8HwzCQy+UYHByE2WyGQCBAZGQkzXQHMVsXg8bGRkxnWWCSr4LxNqVSCYvFgtjYWG6G2lMsy4JhGAgEAqSnp+PKlStobm7G9OnT7VYjzJ07Fx9++CEVU+M5WnNJSACo1Wp88cUXTvu2Eu+gseY/g8GAuro6l5PPyaS1rQ3tHR0QCYUez16aTCavHjcWRiBASkoKZDIZli1diqSkJAzqdNi9e/eEzutr3vr3YbFYsLO83GGhM9tjbW1tYFkWWVlZ2LhxI5566inceOONDmev3LVs+XKUlJTAarXiww8/RFdX14TP6U3DP0ttS8uTkpIgFAq5z1e5XA6j0QiDwcAl5GRymej7JSUlBSKRCDqdDoODg16ObvKz3QCfMWOGx8v0u7u7ceLECTQ2NnKP2cZ1YGAAFy9etDueiqkFB0q6CQkAi8WC/v5+WCyWQIcS9Gis+Y+1WmHQ6x3uq53sbAXUpk2bBpmHs8Wu7hn0Ru0C21gzDIObb74ZDICzZ8/a/XI42Xjr38fBgwddujm3fv163HfffSgpKXHYV3c8Y7V9YxgG69evR1ZWFgwGA959991J1apt+GepLem2zfQNr1Ru+3tkZCSEQmFggiVjmuj7RSgUcte9/+qKhlBhMBi4YmYT3QIyODhot4JNJBIhNTUVAHDo0CG7vd1lZWVgGAbNzc0urXojkxMl3YQQQogX2YpwnTl9GucvXAAAzPaggBowtJSx5WqCM56Ghgb09/d79DqOpKenc4XfPv3006C/ceVqNeaJJJJKpRLHjh3DuXPnUF1djXPnzuHYsWNc9XmhUIg77rgD8fHxUKvVeO+99ya0bcBXbPu509PTAdgn3bS0PPjZiqmpQyzptiW80dHRHtXPsImLi4NQKIRer+feLwCQlpYGgUCA1tZWuxudcrkcRUVFAGi2m88o6SaEEEK8pKa6Gq+8+irefvtt/PtqoioQCKDVat06j9lsxldffYU33ngD2oGBcY9nGAZGoxHnzp1DXV2d13roLlu+HJGRkejp6cHhw4e9cs7JiGVZXLlyxaVjPV0y7WrbN5lMhs2bNyMiIgIdHR345z//Oel6Ig+f6bZYLBi4+m90+Ew3FVELXrakO9Rmum0tvUYWOnOXQCDg+nQPn7kWi8XcuQ8dOmT3PbYboLSvm78o6SaEEEK8wFaEa+QSZavVin9u3Yqa6mqXztPY2Ig///nP2L9/PywWC6IVijF7O9ukpqYiJSUFwNAs5KlTp7yyNFkqlWL16tUAgAMHDqCnp2fC55xsLly4gBtvvBH/+Z//6fQ4BoAiKsqjnuVWq9Wttm/R0dH41re+hbCwMNTW1uKzzz5z+zV9aXjSrdfrIZFIIJVKIRQKuRtMNNMdvNLT0yEQCGAwGKDX6wMdjl+o1WrodDoIhULEx8dP+Hy2Yovd3d12N9UyMjLAMAzq6urQ1tbGPW7b101JN39R9XJCAiAiIgILFixAREREoEOZsMleVZsPYz28KjprtY5qJ8QIBD6rig5M/msYJhYjPT0DYWJxwGIYj7MiXDbl5eVYOOXhMYvvmM1mXLlyBV9++SWAoX+7q1atgjkmGfUNDdBqtTCZTHYzpUKhEBaLBe3t7SgrK0N8fDwuXryIwcFBVFdXY+7cuW4V+3E01sXFxcjPz+eSv3vuucfl8/mDp/8+uru78eyzz+LPf/4zrFYrxGIxigoLUVNTAwB219I2gp72LG9qanK77Vtqaio2bdqEDz74AKdPn0ZMTAyuu+46t18b8M57fPhn6fCkOyIiAvPnz4fZbIZarQbLspBKpWO2TSOB5Y3PU7FYjJSUFJgw1NJQKpV6L8BJyvYeio+P90qtgujoaEgkEhgMBvT09HAz31KpFCUlJbhw4QIOHz6M2267DcA3M90nT57kqp4TfqGkm5AAEIvFE241QVzDp7F21h/42qlZAYwssIRCIaIUk3vWbLwiXCwAlVqNpqYmruXOcN3d3aitreX2786aNQsrVqyATCZDlVKDgoICbhZUpVLBaDRCLBYjKioKlRUVsCpbUVFRgVmzZmHOnDmora1FSkqK27+YjTXWa9aswR//+EfU19fj/PnzmDFjhlvn9SV3/31YLBacOHEC9957L7efcuPGjXjxxRehj0oY83246mqfbljcr9jsaveEkfu3p06ditWrV+Pzzz/H3r17oVAoAnYDbPhn6chCasBQIajhe7vJ5OStz9PMzEzUaYxQqVRB367SarVy3QS8+bMmJSWhqakJnZ2dXNINANdddx0uXLiAqqoq9PT0IC4uDqWlpRCJROjq6kJzc7NHK25IYFHSTUgA6PV6NDY2IisrKyTuEAcSX8baWX/grVu3Im7TWhQWFgYktvGMNTvvLWazGSqVCgqFwqNq0WOZaNzd3d04evQoDh8+jE8++QSlN64d93tGLvnW6/W4fPkyent7AQDh4eG47777HCbmtgR6ZAGfouJiNJ3sw+DgICorKzFz5kyu6I5NR0cHGhoacM011zhNxMca6+joaFx//fXYs2cPdu3ahSlTpkAmk4378/qDO/8+Ll++jF27dqGnpwcqlQozZ87EK6+8giVLlgAYWnUysmf5RP5NsyyL6upq7N27F9LMKeMeL3Yw+zh37lz09/fjyJEj+Ne//gWRSMTF60+2z9KMjAy0trYC+KaQmg3t5578vPV5mpWVhbqKy3aFwIJVT08PzGYzJBIJYmJivHbepKQkGAyGUYl8YmIipk6dikuXLuHw4cO45ZZbIJVKMX36dJw5cwYnT56kpJuHKOkmJAB0Oh3OnTuHxMTESZ0IBgM+jDVrtTrtD8xgaGlyQUHBpFtS5mx2vnBE4ucps8mEzo4ORISHey3pdndVAcuy6OnpQXNzM5qbm/HSSy/h0qVLdseU3jj+69qKcLEsi9bWVjQ0NHDF1jIzM5GRkcHtza6trcXJkyeRv3Apwp20GxOJRCgpKcHp06ehVqtx6dIluxs0BoMBtbW1qKiowMWLF7Fu3boxkyJnY71gwQKcP38eXV1d2LNnD9auHf8mgz+48u+ju7sbu3btQl1dHYChpdL/93//h+985zsOl4oyAgGys7M9jollWVy+fBn79u3jii+N9+kjkUjGvC4rVqxAf38/qqqqcOutt+LIkSOjbqz4mu2zVCAQwGQyQSAQQCKR4MiRI4iOjkZRURHNdPOAtz5PMzIygIrLGBwchMlonNTbfybKtrR8ogXURgoPDx/zZvp1112HS5cu4dy5c1iyZAmioqIwZ84cnDlzBidOnMCGDRu8GgvxPUq6CQkiFosFBw8eRHt7O1JSUrBo0SLqk8oDjU1N4y9NVqnGXJocKOPNzt9+++0oXnRNQGJzxpVVBXl5eWhvb0dTUxOXaOt0Ou5YW8JdXFyMhQsXYsGCBTh8uQkDWq3DmycMhhKRzMxMaDQaXLp0iSs4FR0djf/f3pmHN1Wm/f97krRpk+57S/eVtmylbG0BBdkRFWVRVBRFZcQRnVFHxxkd/Y2Oo6/vO477goiCDoiIwLDvIBSkUKArXSnd971Ns9y/P0qOTZumaZrT9uDzua5ckJz0zp3n5JznuZ97i4iIMDCsdTodDh48iMrKSihDRiIsLMzkd7K3t0dsbCwuX76MiooKKBQK3hMil8sRGhqKrKwsFBQU4OOPP8a8efP63WdWIpFg0aJFWL9+PS5cuICxY8cOe29LW1sbjh07hvPnz0On00EqlWLKlCmYNm2aYLqrVCp8+eWXfFstW1tbTJkyBfAK4PPFjTFixIheN9U4jsPixYvR1NSE+vp6zJ8/H8nJyVY3AsxBb4D4+vqivLwcarUaHR0daG1thUajgVQqHdY1NBjWQaFQQKlQoKW1FQ2NjVYpLjYc6ejo4CORBjOMPiAgAEFBQbh27RrOnDmDuXPnYuLEifj8889ZMTWRwoxuBuMmYfv27Vi3bh2/0AM6Q//ee+89tiM6zNHnivXF4cOHMXHiRISFhZn0fA4GpgqHdfXO35kYP6w2fvqKKgCAbdu2gYh6tGmysbHBiBEjEBAQgIULFyIhIQFubm78cZeT57B161ZwMF6Ea86cOcjPz0dJSQmICDKZDGFhYUYNJ71H2c7ODkFmGod64/3q1asoKCiAQqHgF8J+fn5Ys2YNduzYgeLiYvz000/Izs7GyGmzeA8VEaHpxkZAU3Mz5HZ2PYxAf39/xMfHIyUlBbt378YTTzwxpOeXdDqU3uivXVpWhpDgYHASCXQ6HX755RccO3aMr64cHR2N2bNnWzU81Bg2NjZobGyETCbDpEmTkJSUBIVCgYzqJkilUuTm5hoUVZPc0LestBSjvHoP+ZXJZLj33nuxYcMG5OTk4Pbbb8exY8fg4OAg6Pfpjt7oDggI4OcbR0dHg/7cwy0ihyEMzi4unUZ3Q8NNa3RXVlaCiODk5CRYSk1LSwsqKirg6+tr8BlTp07FtWvXkJKSgmnTprFiaiKHGd0Mxk3A9u3bsWTJEr7Qkp6SkhIsWbIE27ZtY4b3MKSsrAz/+Mc/UMWZF/au97hyHAc/Pz9EREQgIiLC7IJZ1sy9Nrdw2MmTJ4ck/7Q3+ooqADo3FIBOQyIgIIAP+/bx8YHkxngZ83iMjI7GvHnzcPToUbR3MarkdnaYPHkyGpuaoGut4/8+LCwMNjY2PeSo1WocPXoUADBt2jTIjLynN3x9fdHS0oKSkhJkZWVh3LhxvFHm7u6OVatW4eeff8axY8eQlZWFZoUrJk2ahLq6OuTm5kKr1cLVxQVXr15FXl4ewsPD4e5quMEza9YsZGVloaqqCqdPn8a0adPM1s+a6FMEpBIJZs2ahd27dkGr02Hs2LGoTEvh+157e3tj3rx5AwoXN0VTUxPKysoQEREBjuMgkUiwZMkSuLq69jCIPTw84O7ublAMT6lQ4MKFC2hrb8fVq1cRExPT62cpFArs2bMHCQkJSElJwX333Yd/rN/E/y57w5rdD/Th8nqj29XVFU5OTnw7OZbP/dvB2dkZpaWlN3Vet7V6c5siPz8ftbW1kHRLa9FvypaXl+NYRh4CA0Jw37oXoNVqseG/h+Dj42Mwj8d4OAqmI2PgMKObwRgCbGxs4OfnZ3TB3V+0Wi3WrVvXw+AGwO+EPvPMM7jzzjuHlcdxsLDmWFuL6upqvP322/jggw/Q1taGZU/9ETKpFJobxl53OHTmoMbFxSE3Nxfl5eUoKSlBSUkJjh07BoVCgfDwcDg5OWHu3Llwd++5xB5IZXQiQn5+PtLS0vjHqVOnkHDnsj6/a9kNL+RAkEilcHB0hMQKv199SHdfzJ8/H5Mm9S80vrq6Gm3t7Zg8ZQoa6ut5o8rZxQUcx0GlUsHDzg4REREGHvLunD17Fo2NjXB2dsakSZNwtb6t1/caIywsDK2trairq+MrmusLdEkkEkybNg0RERH48ccf4ePjg7q6OqSnpwMApBIJVB0dIJ0OKo0G6enpcB8ZYuDFsrOzw7x58/DDDz/gxIkTiI2NHfTqxV1TBByUSpSWlkKtVqO5pQUnT56Eu7YVSqUSM2fORFxcnCAeoZaWFhQWFvLGfdfK4qY6JnAc16MYXkxMLC6mXuQjLEwZ0eHh4di5cydmzpyJ3bt345a9e7FgwQJAYK+X/l566dIlAJ3RExUVFbzRnZ+fD4Dlcw93rHk/1W+wNDc1QavRQGrFQpfDgebmZrS0tEAikRhUF7c2Pj4+qK2tRUVFhYHRzXEcpk6dim3btqG0pAStLS38sf379wOwfg0VhnDcXFcHgyESHBwcLO632p2TJ08ahJR3h4hw/fr1YedxHCysOdYDpb29HcnJyVi9ejVv/CUkJGDlypVQtbdj69atAIyHJt9+++0YOXIkbrvtNjQ1NSEvLw85OTnIy8tDa2srLl++jI8//hgcx2Hy5MmYP38+5s+fD0VwFLKzssyujN7U1ISqqipUVlYaPP7973/3+D4JZnxnfVGwgWBra2u1/Ftzi+n114jU6XTIzc0FcMOoMhLCLJVKMWHCeJObX2q1GqdOnQIAzJgxw6JCRxzHISYmBhcvXjSoaN4VHx8fPPbYY8iqaca5LvmBWp2uRyRAbm4u3N3dDQzXUaNGITU1FXl5edizZw9Gjhw5aKGO3VMEmlta8PPp0wbvkcvlWLt2rcXhoERk4I12dnbmv19rayuuXbvGp4VwHAdvb+8BeXgdnRwxPm48ghTmRZ4kJCRg8+bNWLJkCX45fx4urq5ITEy0+PPNQX8vff/99wF0LvaJCHK5HBzHob29HRzHMaN7mGPN+6lcLoednR3a29vR2NgEVzdhUzcGG72X28PDw6qdM7rj7u4OqVSK9vb2Hi3YoqOj4ebmhtKyMhw8dKjH3w73GiqMX2FGN4MxBOh0On4x11dYYF+Y60m0hsdRjFhzrC2lo6MD586dw88//4z29nY0NzcjLi4Of//73zF//nxk1nQa4MuWLeu9P3AXb7SjoyPGjRuHcePGQafTobi4GDk5Ofj5559x+fJlJCcnIzk5Ga+++ioefuGvUKvVJnOY9R7Pqqoqg2JhXZHL5YiJicGoUaMQGxuL6Oho7DxzAc29FA4DABuZzCr9nIkIWq0WUql0QIZdWloa7x3oDQ6d3pv+LkqLiooM8nSNodVq0dTU1MPL2ZWSkhK+hcxAxs5YRXMfHx+D8ZPJZGhsauqht4TjoOsSOaNSqdDQ0NBDb33v7ry8PHz33XdYsWKFxfr2h+4pAhKOg42NDdRqNa+3SqVCZWWlRYUHq6ure+Rd64vR1dbWQltWyEcWeXl5ISgoyCo1FhwcHQx6gPfl8b777rvxv//7vziTV4yDBw/C2dkZMdHRgrXv099LS0tLAXR6vnU6HZycnPjwYqVS+ZuMqBIT1rqf6nF2dkZ7ezvqG+pvKqObiATpzW0MvSe9vLwcFRUViIyMNDg2efJkfLD1J+N6YvjWUGEYwoxuBmMIaGhowMGDB61S1MdcT6I1PI5ixJpj3V80Gg3Onz+PkydPorW1czHt5eWFH374AYsXL+6x4DHZH7jLYrwr+lZTgYGBWLFiBYqLi7Fv3z7s3bsXBw8e5D/XFB0dHSgqKuLlubu7w8vLi394enri3//+d4+dfo2rj9HCYXrUGg0mTJiALVu2YOLEiX0PWC+o2tuRn5+P0NBQ2FnguSQirF+/Hr///e9xx+q1sLezQ1t7e68Fz+bNm9fvxWj3/tu90dHRYfJ4YGAgZs2aBT8/vwEviO3t7RETE4MrV66goqICp0+fRlJSkkl9ZDIZXF1cUFdfD41GY1JvV1dXTJ8+HUeOHMGzzz6L+fPnD8o11j1FwNnZGbNmzcKhQ4dQV1/Pv27uOelKdXU1H2rfFZVKhczMTCgVCtgRwcPDA0FBQYIUMdNqtcjJyYFGo8GoUaNMvveZZ56BbtP3OHv2LLZv3469dnZo6XLNm5tCYg76e6n+dxESEgKNRgMXFxfWKkxEDPR+2h1nZ2dUVFSg8SbL666pqYFarYatre2g3Ne8vb1RXl6OqqoqaDQag/m2ycjmaFeGaw0VhiHM6GYwRM60adPg7+/PV0TuDsdx8Pf3H7JiR79FtFotLl68iBMnTvALfzc3N8yYMQOxsbEmC7IMtD+wv78/Vq9ejdWrV6OjowOf/7QPJ06c6PPvJk2ahPj4eD7MrTvGQutGRkcb9c47Ozlh4qRJOH/+PAoKCpCUlIS3334b69atG/Rqq01NTVizZg2+/fZbAJ35sIvvugtFRUVmRRWYi77/dl/Y9tHLViKR9DCMB4KrqyvCw8ORk5ODQ4cOwcPDA1FRUWbr09f7EhMTceXKFVRWVuLFF1/Ep59+ahW9TdFXITw95p4TPV1TBHpDrVYjIS5OUOOyvb0dVVVV0Ol0uH79usn8cACYO2cOiouLUVJSYmBwA8ZTSAZKVVUVAGDmzJl8XveFCxcAsCJqv0X057yxqanP6Awxoa/S7+3tPSjzlouLC+RyOVQqFbKzsxEbG8sfM7fDyW81olEsMKObwRA5UqkU7733HpYsWQKO4wwMb/1E8c033/DFfnpjsAshiZneqoDrdDqkpqbi+PHjqL/hcXN2dsatt96KMWPGmLUYMZVL2l9sbW0REhJiltEdExMDLy+vfn/GyOhoREZGIjMzE/UNDXBxdkZ0dDQkUini4+NRnXER27dvx7PPPoujR49iw4YNJouIWZPU1FQsX74cV69ehVQqxRtvvIHbV6wAx3EWRRWYwt/fH5KLGT3ajHVFLpf3apSo1WrIZDJBFnd+fn5oaWlBZmYmtm/fjkceeYS/3p2dnfmFniV6S6VS3H777Xj99dfx2WefYeXKlfymQUZ1355mdzPGWq9rS0sLXnjhBVRLTHvnhEwR6FCrTZ5ja6BUKhEREYHs7GwUFBTA0dHRZEoC0GlcG6Nr6GlUVJRVfl86nQ4ymYw/Lzqdjo8+YJ7u3x4Ke3vY2tigQ61GU1PTTbHxolar+Wr8g7k28vb2RllZWY/7EItovDm4ObajGIzfOHfffTe2bduGESNGGLzu7++Pbdu2Wc3Dweismvyv997Dxo0b8cMPP2Djxo3413vvYf/+/fjoww/x008/ob6+Ho6Ojli4cCF+//vfY9y4cWYZ3NXV1Th79iwuXbqEzMxMXLp0CWfPnu1zw8QUQYGBnX1zezluqYHSVedzv/yC6poaaDQaVNfUdD6vroadnR22bduGDz74ALa2tti5cyfi4uJw5swZi7+PORARPv74Y0yZMgVXr16Fv78/jh8/jj/96U+GRseNKtJeXl6dRo2FBolOp8OuXbv6NMbCw8N7NXoyMjKQkpJiUUi0OYSHhyMkJAQdHR347rvv0HKjCi7HcQgPDzf5t315egIDA/Hoo48CANasWQO1Wm09xW9w5swZjBs3Dh999BEAICI8HBzQ43dtSYpAR0cHzp49i127dpn9fqHx8fGBt7c3iAiZmZkmP/NaUREaTfxuCJ2h4foUEmvg5+fH69TY2MgXVDO3UCHjJoLj4HTD0C4tKUF9fb3RqDsxUVVVBSKCg4MDlErloH1uYGAgEhISMH78eIPXp02bZnJDi0NnhBmLaBzeMKObwbhJiI6OxjPPPIOHHnoI99xzDx566CGsW7cO0ayNhNXQtynqHt7a2NiI5ORk1NTWQqFQYO7cuXj66acxYcIEs4ua6HNJu+9wq1QqpKenW2x4cxIJ5s+b1/n/7sdu/GtJDjNgns4cx2Ht2rU4c+YMwsLCUFRUhOnTp+Odd94RxGPY0NCA5cuX48knn4RKpcLtt9+O1NTUHiHb1trgICLs3r0bly9fBofOVlFyudzgPXK5HLGxsQZtt7pSW1uL+vp6tLW1CdbajuM4LF26FG5ubmhoaMDWrVv53FwPDw/Exsb20FsqkcDW1hZ+fn59yn/77bfh6emJtLQ0vPvuu1bTW6vV4uWXX8bUqVORm5sLf39/PPjgg1hx//1YtmwZHLstRJ2cnLBs2bI+Nxq7RwQdOXIEtbW1Zulkbkj+QImIiIBSqURHRweysrJ6NWTMbYNnzQ0dR0dHvP322zhx4gR/P7wZPJyM/lNdXY36ujoAQGVVlVU2i4eawejNbYzeittJpVIkmehOQOhM9WFF1IY3zOhmMIYAZ2dnLF682GqLlMwbxmBzczNCQkIwevRohISEoLm5GVu3bhX15DcQtFotUlNTDf4diKyubYqMYSeX4+mnn8aUKVP61V6EiPrMJc3NzbXYe6DPvbbUQDGGOfmvubm5vGE9fvx4XLhwAcuXL4dGo8ELL7yARYsWmfXblNvZYeTIkZD34UU7f/48xo8fj++//x4ymQzvvvsudu7c2aNvubU2OIgIe/bswcWLF8FxHKJjYhAaGorJkydj7NixiI6OxtixYzF58uReDW59D3QAGDFihKCeQnt7e9x3332Qy+UoKirCf//7X+DGb8rDwwOTJ09GbEws3NzdERsTi6SkJEyYMKGHMW4MNzc33th+7bXX+O80ECorK/HFF1/gzTffhE6nw4MPPogrV64gNDQUQOfvet3TT+O2225DfX09brvtNjz99NO99qvVarWoqKhAWloaUlJS+OvJxsYGSUlJmDNnTp8GtalQe2sjlUoRExMDqVSKuro6XLt2zej7zC3mZu6mQm84OzujpKQEhYWFfLV2Nzc3VkRNZJh7PzUH/b1U021uHehm8VBSVVWFpqYmcBxnUcqVNSAilJSU8M91Oh10REY3R/WbujoiwVNfGAOD5XQzGEOARCKxWrERnU6Hffv2mXyPsT67Nzvbt2/HunXrDHqY+/v747333sPdd9/d5983NTUhPT0daWlpSEtLw8mTJxGeOMPk37SrVCgvL++zTVFTUxPf/3rs2LFoaGjoM5e0t7ZN5mLtHGZz8l9VKhWKior4wnBOTk747rvvMHPmTDz99NPYs2cPxo0bh++++85kWBzHceBM7OATEd5//30899xzUKvVCAoKwpYtWzB58mSj7zVns6Cv64WIcODAAZw/fx4AcNddd0Hm6cnra+55qqysREtLC2QymdV655rCw8MDS5cuxebNm5Gamgq7wAi+UFdnf3EXg/fbSCSAtjNcvKqqqrO6eS+VfB944AF89dVXOHLkCJ588kn87zdbLQrZJyKcOXMGR44cgVarhbu7Oz799FPcc889AIDSG7ni3Vt76VMbwsPD4e7aaRTqdDrU1NSgqqoKNTU1BovS6upqeN44Z9OnTwcAqLMKjFYv12MqRUAIFAoFIiMjkZeX1+tvSp9C0tTYaHJT8OjRo8jKysLMmTP7TCkwhkQiQVFREYiI35zw9/fH1atXATCjWyz0dT81F3M3XiMnjBbV2iM1NRVAZ+9soSKPTKHT6fD++++jrq4Oa9euhYeHBz/fenp6wsPDAw319b/WfXFxAcdxPeZbxvCDGd0MxhDQ1NSECxcuYPz48f2usNudom49a42hUqlw/vx52Nvbw8bGBjKZDDY2Ngb/5zgOCoUC9vb2og9R2r59O5YsWQIigo+PDx555BF8+eWXKCkpwZIlS7Bt2zbe8G5vb0dWVhZvXOsfxrxKfRndQM8Qzra2NtTV1aGlpYV/XLlyhT/u5eUFc1NE29raLDa6gYFXRu+KvshMX3RfBHAch8cffxxTpkzBsmXLkJ2djRkzZuD111/Hiy++aHQzqkOlQll5OXx9fGDbbZe/rq4OjzzyCHbs2AEAWLx4MdavX9+rYWitDY4jR44gOTkZALBo0SKMGTPGrMJhXdHpdCgoKAAABAUF9Ss6YiCEhYVh7ty52LdvH/Lz86FQKPhogN7Gur6+HpmZmeA4DqNHjzY6NhzH4eOPP8bo0aOxf/9+pGdkGFTgNYe6ujrs2LGDzz+OiopCWlpajzDPrq29pBIJHBwc0NzczHvY3EeGQK1WIy8vzyDCRaFQ8G3w9AZ3V/Sh9sb6dIeHh3dGLFiwSTUQvLy8eu0qAPyaQmKsfZ/e1ImJiUFubi7KysqwefNmBAQEYObMmf26HzQ1NSEkJAQ+Pj5wcnKCg4MDOjo6oNFoIJVKBWmfxrA+pu6n/cHcjdeBbBYPNjqdDleuXIG/v/+QFZeVSCTw8PBAXV0dLl++jJkzZxqsKzo3R43Pb0LVBGFYB2Z0MxhDgEajQUVFhUEfXEvRt2/pi9bWVpM9mzMzM/n/y+Vy2NvbQ6FQ8I+uz7v+v6OjAzKZbNi0CdFqtVi3bh0fOmpvb4+xY8fC3t6ef23VqlXYvHkz0tPTkZOT02tIlq+vL0aNGoVRo0ZBKpWiqL3v8O7Gxka0tLTwxVcaGxuRk5Nj8B6O4/he2La2trA1M2rcvktP1e59PAcLnU6H8+fP4/Dhw3CJHN3n+48ePYqCggIkJiYaeAnHjBmD8+fP48knn8Q333yDl19+GceOHcM333wDSBU9PrOlubnHeTp79iyWL1+Oa9euwdbWFu+++y7Wrl1r0qtibhEsk4Wrrl3DqVOnAADz58/vUfTGXEpKSqBSqWBnZ2dW3rQ1mTRpEiorK1Hc0Xntx8XFQaFQoKGxES3NzWhobISHhwc/lk5OTnBzc0NNTQ3S0tIwZswYo57NyMhIvPzyy3j11Vexb98+hIWFmR0yf+HCBezfv5/34MyfPx/jxo3rsfjtHq3A3cg95yQS4MZvJDc3FxEREdBqtbCzs+MNbXMMQw8PD7i7u1uti4A16Gpw19XVwcnJyeC13tr36dvgJUUGobW1FadPn8a5c+dw/fp1bNy4ESEhIZg5cyb8/f371EGj0cDHxwf29vZwcnJCQEAArl+/DqAzx1tM3szfMr3dT/uLuQZeXV2daIzu/Px8NDU1wcbGpkda0mAyZswY5OTk4MqVK5gxY4bZzpmBOnEYwsKMboboMMebFOMhvhuPvidkX+gXoGq1GmfOnDGrHRQAhISEwNbWFmq1mn9oNBr+/wqFgjfKVSoVVCoV3/bKFPpCbVKplPeed/eiFxUV9TDWFQqFIEbjyZMnDULKjdHY2Ijt27fzz11dXXnjWv+IjY01mHTz8vLw2sdf9tlaqaOjAw0NDbzR7eDgAHd3dyiVSiiVSigUCtx9990G372xqrHPtk36hT/QuWg6d+4clEolfH194eHhMSibHrm5uThw4AC/0dO9RV13pBIJOI5DYWEhCgsL4eXlhYSEBIwePZr3jG3cuBEzZszA2rVrcfDgQYwbNw5f7NyPkJAQAJ3t2Upv9B4tLStDSHAwwHF499138eKLL0Kj0SAsLAxbtmxBfHx8n9/B1mZgfamvX7+OwsJCAMCcOXMwadIks+QZo6GhAQAQHBw86JtWHMdhwYIF2Hr0NOobGnDp0iVwHAedTgdXFxdcvXoVeXl5fKi2RCJBTEwM0tLSUFdXhytXrmDMmDFGF3l/+tOf8O2336K5uRlHDh/GgoULTerS3NyMnTt38ptTQUFBuOuuu3pdqJsbrSCRSBBnYU/t/qQIDCZVVVXYtWsXJkyYgNmzZxsc6yuFRKFQYNasWZgyZQpOnTqF8+fPo6CgAOvXr0dERASmT59u1jUEdOZ3+/v780Y3K6L228NcA6+oqAiVlZXw9fWFj4+P2YUIzWo5aJYk87l06RKAzuiSodxEioqKgq2tLerr61FUVITAwEDI03L6XH8MRooSw3KY0c1giAwiQkZGBg4ePMgv2qVSqckiYXK5HAEBASYnkdtvvx06nQ7t7e1oa2vjPeOtra0Gz7sf0xteWq0WWq0W7e3tPWR39aJ3xcbGpof3vLth3v21vnKsym4YaH1x//33Y+XKlRg1ahR8fX37nGBbW1sRHh7eZ76n3sDWo1QqMWrUKIP3dd9s0LdtMiU7IiKC17GxsREajQb19fWor6+HjY0NfHx8IJPJBNmdb21txbfffssbRfb29pgxYwZaHdyRkZHR69+NjI7G8plJSE5OxoULF1BZWYmffvoJR44cwZQpUxAfHw+5XI5Vq1Zh8uTJWLp0KTIyMvDN11/jlltugZeXF/bt3w+pRIJZs2Zh965d0Gi1cHBwwHsvPwcAWLZsGT7//HOzDCsiQk1t32HxUqnUqLySkhK+QNjMmTORkJDQpyxTjBo1CnV1db2GwguNVCpFTGwszv/yCzputPnq+tvsGqqt39iJjY1FWloa6uvrcfnyZYwdO7aH91gul+OTTz7Bxz/sxvnz5zFm7NhePakZGRnYvXs32traIJPJcNttt2Hy5MlWiVZQq9VDNrZCwXEcOjo6cPr0aQQGBiIqKsrwuBkpJA4ODpg3bx4SEhJw8uRJXLx4ETk5OfjDH/6AxYsX4/XXX+9xzwJgEJnl5OQEf39/pKSkwNvbm+Vz/wYxxxCUSqWQQor29nYUFBSgsLAQHh4e8PX1HXbXpj7VDBjc3tzGsLGxQUxMDFJTU3Hp0iUEBQWZtf4YLhGHDOMwo5shaohoWIUACk1TUxP27NnD5zs6OTlh1qxZkEql+P7773v9O3OL/0gkEt64Ndd404fJ9+ZB1/fs7Wqot7a2goigVqvR0NDAbx6Yg0wmM2qQ6/81FULflUceeQQzZ840+3MdHR3h6elpMt/T09MTAQEBFnl9+swldf015NrFxQWTJ09GeXk5ysrKoFKpcP36dRw4cADBwcGYM2cOfH19e3xGf68XjUaDa9euoaSkBDk5OZBIJJg0aRJuueUW2NnZIaO6qc/8V2dnR8ydOxe33HILzp8/j7Nnz6KpqQkHDx7EiRMnEB8fjylTpiAmJga//PILfv/736MZwLHjx3l5rl08jk1NTWhqaoJMJsMHH3yAxx9/3Oxr/tixYyiubenzfVqtFunp6YiOjuaN0LKyMj6kOSgoyGiRNksY6oWnOdEmXQvLSaVSjBo1CpcvX0ZjYyMuX75s1Dt666234kxeMVJTU7F71y7MmTsXra2tvPdVpVJhz969KE09C6AzlWPx4sVG86y709bWZtZ3G6zWXoOJvsr82bNnsWPHDjz++OOwdCnn7OyM22+/HYmJiThx4gQ4jsOPP/6IHTt24N5778Xf/vY3REZGAui8Jg4fPswXAZ08eTIcHR1RW1vLjO7fKBKJpE9DcOTIkYhwtkNVVRXKysrQ0NCAqqoqVFVVwd7eHr6+vkNu4OpJT0+HRqOBp6fnsAjTHjNmDFJTU5GRkYEFCxaYV2+CMaxhRjdDtHSvXAv8evPBMA8vt7e3R1xcnEGOrik6OjpQUFCA8vJyFBUV8S1uEhMTec/vsmXLsK+XfD43NzdBvocemUwGmUzW6/fpXpmaiKBSqYx6zru+1v2YTqeDRqNBY2Njr8XjdDodnJyc+OPV1dVYv369QesSJyenPiuMd8ff3x/ytByT1UMH2k7IZC5pt+JNcrkcQUFBCAwMRG1tLcrKyvhQ7q7GBul04CQSk9eLu6thDjURoaysDIWFhfymSWRkJObMmdNjM8bc/Fc7OztMnToVU6ZMwZUrV3D69GlUV1fj9OnTSE5OxpgxY5CQkID169fjP4dPYceOHXxBqNbWVly4eNFgQ8Xd3R2rV6822+A+e/YsTpw4Aa9R8QgPD4dcLjc6Hp6enlBdz0VtbS0uXLiA2NhYNDU18RWaAwICBlyMrrKyEm5ubkOSk9+dhoYG3ssNADqtFs3NzdB1iZzpXgxJKpVi9OjRuHz5MpycnHptKTZ79mxkZGSgorKyM1f/BgqFAkSEtrY2eEokmDZtGqZNm2Z2AUd1F31703kwW3sNNrNnz0ZxcTFKSkqwbds2JNy5jPdwWbIR7ebmhrvuugvTpk3D3/72N3z//ff47rvvsHXrVqxcuRKTJk3CG2+8gYaGBiQkJKCyshLPPvssnn/+eQCd0TzD4bfMMA+ZjQ18fHwhs0JlbnMMQYm2Fd7e3vD29kZLSwvKyspQUVGBtrY25Ofno6CgAMnJyYiPj0dISEiP3+tgOVf0oeVjx461umxLCA4O5tcyubm5gMeIYVlvgmE+7C7JECVdK9d2RR8O6UntfK7xcMTOzg4RERF9vk+n06G4uBhFRUV8+Pjo0aMxa9asHp6F6Bv5fEVFRWhqaoKjoyMCAwMhkUjMzhcfLDiOg52dXb96EhMROjo6eg1z179WWVmJeTcq+QKdntEDBw4YyJo3b57ZHnGg0+O7fft2KD39oVKpeq0eao12Qv3NJdUXZXN3d8eECROQn59vYBhnZmWhtaUFLUa+b/fwYaCz6E1eXh5aWjo9wkqlEmFhYSZ7efdHZ5lMhri4OIwbNw5Xr17F6dOnUVRUhNTUVKSmpiIiIgI2fiEGFZhVHR3Iy8szkFNRUYGTJ0/i1ltvNetzIyIicObMGYSGhmLEiBEA0OviRe7hiPT0dLS1teHChQt8wSF/f3++R7SlNDU1ITMzEzY2Npg0adKQGyvdQ7V1RGgzkiLS/X0ymQxjx441aSgXXbtmNBRcf+05OjjgkaUr+PPRGy0tLejo6OCjAoKDgqDu6EDljdoCxnQe6LU4nKOopFIpli5dik8//RSlpaXIy8tDREREvzbWjBETE4OtW7ciNTUVr7zyCnbt2oUNGzZgw4YN/Hv099Lm5masW7cOy5Ytw2233Wb9L8kQDJlMBjd3623E98cQVCqVCA8PR2hoKKqqqlBaWorGxkZkZmYiIyMDrq6uGD9+POLi4gCYdq5Y07NbW1uL69evg+M4jBkzpl/rA6HgOA7z58+HUqmEv78/Mmua+deHY70JRt8wo5shOszps7tv3z5ERUUN2/wW1Y1+zj4+Pr16iaqqqpCfn8/nSDs5OSEsLMyksS6xYkuo4YbekyyXy02G5BYWFqK6upr3/Gu1WsTFxeHixYuQSqWYN28eYmJizA4fU6lU2LJlCwoKCuAj6zRAi4uLh007oa44OzvzixWgM0eturraZLEzoDN8WKFQID8/n28FZmNjg+DgYLPy3S2B4zhERUUhKioKxcXFOH36NDIzMzvzxusNw4dtbWzg4+uL8rIyA6+sufn7QKc3b82aNchv/vXve1u8ODg4YPz48bh06RK/+eDn54ewsLB+fstuEPE54cPF0909BJvjONja2qKjo8Pgd2MsVLurwa3VarF//34kJSXB2dkZWq0We/ftM/nZnERismo7EaGkpAQHDx6Er68vVq5cCY7jILOxQXRMDDxvLMj1C339v93TMfrLYC30B4KzszPuuusufPfddygtLYVMJuPTjrpibGOtL8aNG4edO3fi9OnTmDlzJj8OSqWSv5fqr4t9+/bhz3/+s/W+GENwtBoNmptb4OCghNRK96D+GoISicTA++3g4IDLly+jrq4Ohw8fxtGjRxE65RajEW3633RsbKxZm0mm0Ol0KCoqwtmznWkuISEhcHR0HBZGNwCTm90M8TH0Mz5jSBFjJXBzKtc2Njb26A88nGhtbcXZs2cxe/bsHkZ3c3Mz8vLy+MrhcrkcoaGh8PLyGgJNxUeL0g0B8YnwGhWPW5Y+gPa2NgQGBqKoqAh29vbgOA512ZeN5jz3kNXSgs2bN6OsrAy2trYYNXo0XF1dMWLEiGHrBeuKnZ0dRo4c2WshOz36Pu5EBI7j4Ofnh+DgYKsYheZEWfj7+2PZsmWoqanBmTNnkFZRb3BcqVRi8qRJOHToEDpuXBe/+93vEBoaalJ+SUkJHBwc+LxUOzs7oFnd6/u70tjYyC+8fHx8+M0u/RhZQm1tHerr64fV5pizs7NB5XypVAonR0fU1dcbFM4qLCxEeHh4ry23Dhw4gF9++QW5ubl4+OGHceHChV5TQPTo79PGUj2qq6uRn5+PtrY2vptCe3u7QQqL3sNWXV2NqspKeHp5/drmzMLNr76iqKyx0LcWkZGRmDp1Klod3FFRXm7yvV3z8s2lo6MDdz72FP/c1cUFs2bNwqFDh1DXpbNFY2Njjz7qjOGLWq1GSUkxQkNDrWZ0DwSlUokFCxZg1qxZSE9PR0pKCkpKSvq8f+Tm5iJywmiL78eZmZk9UvLKysqQmZkpeEqeUJgz3w6XHPrfIsPTDcjoFZ1Oh8LCQly5cgWFhYUD7rOoh4hQX1+PyspK1NfX9+kZGypKSkpQcMNT1Bfm9pAcLnR0dODq1atISUnhF+ZBQUGYOHEiM7j7gb4SuP7/jjfC8B2dnPjJWa1WY/369SZ7nNfX12PDhg0oKyuDQqHAQw89xHvY9bv6Xl5ecLmRzy12iAhubm6YMGECwsPDh8QL6+7ujttvvx1TEhJ6jQDR01fObkVFBXbt2oUtW7b0O72itrYWGRkZICJ4e3vzRnt1dTXOnz9vkReEiJBf0HnvGjFiRL9SK4Sk6/Vi6j0NDQ1ISUlBdna20ZDxpKQkuLi4oK6uDl9//TUKCgrM+vzu9+mWlhZcvnyZD++3tbXFHXfcgccee8xozQiO4+B4YyPA0cFhQNeiTqfrM4oqNzd3wPOjtebb1tZWhIaGdtbI6KOiuz4vvz+UlJSY9b6+NsEZDHOwtbVFXFwcVq9ejUWLFvX5fpVKhaysLJSWlqKmpgbNzc0GG4WmyMzMxNatW3sY9m1tbdi6datBDZihprGxEbt378aVK1eGWhXGABn6LS6G2RjbldMXyhpI/vJwD6UjIuTk5OD06dO4du0avEaZ10d0OFSfNAeNRoOzZ8+ivLycz9v28vJCaGhon4YHwzhdi7touxVX8vHxQWthNqqqqvDZZ59hwYIFGDdunMFivbKyEps2bUJTUxOcnZ3x4IMPwt3dHfVmRIYMFGvnkvbVYk2Pp6cnNBoNWlpaYH8jImAoMac9SnFxMYgILi4uBqkkVVVVyM7OBgDEx8f3a9Oqvq4epenp0Ol08PT0RFRUFN8Wr7CwsLOo24ULaG/vX92IioqKzjBKmWzY9VI1db2Eh4cjWCFFQUEBKioqUF5ejqqqKgQGBsLf358fdycnJzz00EPYsGEDampqbuTg910Yret9Wt+GjIggkUjg7++PwMBAkyHo1qSoqMis/t/FxcVwd3eHXC43u/ibHkvmW61Wi5aWFrS2tqKlpQUHDhxAZWUlmps7czzNnRPNbbWmx9zvNlzTuBjixdx5q7KyEpWVlQavSaVSPhVNLpcjMzMTzs7OcHJygrOzMxwcHLCvj9QXSyJDhEIikeDChQvwjB2P1tZWKBTDI9KG0X+Y0S0S9Lty3WlsbMTWrVuxbNkyiwxvc0LphqoSuEajwZUrV3DmzBneI6nPA6qrrTXI7eyOVCod8lY8vaHT6VBaWgoASElJQXp6Ourr6xEdHQ1HR0eEh4ez9itWoHvoaWRkJB96On3kE/jxxx+Rn5+PnTt3orCwEAsXLuTzVvW75p6ennjggQcG7XwMtBBSd4jI7J1//aK+rq4Otra28PHxga+v75B4ZG1tbQ3as3WFb8/mpkRtbS1qa2shkUjg4uICNzc3cBzHeyPj4uIwf/78XhdO3Tc4ACAt7QpcdDq4u7sjOjqa/1uO4zB27FhkZGSgvr4eW7duxbRp0zBjxow+F2Y6rRaFNzy/QUFBwyKXuzumrhe5thUjR46En58f8vLy0NjYiIKCApSVlSE0NJRv8+Xi4oKHHnoIX331FXQ6HWpbtVC1t8OYH5dDp6HedQPC2dmZbwUYGhpq1m+PiNB0w/hsam6G3M6uXwtlvRF9/fr1zjQMr4A+/yY/P5/PzZfJZJDL5bCzszNY6Le1tcHZ2RmOjo78+e5rvo2OjoZcDt641j/auxWJy+8S7eXi4mLQrcEU+t+4SqUyCNvXh+5v374dKpUKzzzzDIDOfFL5+SsmNyLkcvmw2Jxn3FyY6zTR//b0v2G1Wg2tVssXVwXQZ3qVMbp3bBhKHBwcEB4ejgZ0bt6GhIQMtUoMCxl+Mz+jBzqdrs9dud27d0Mul/drx1mn0+Hq1RyT78nNzcXUyKBB3club29HSkoK388X6FwsTJgwAZMnT0ZxB9fr4kWPVqvFF198gWXLliEgoO9F1GChj1Zob2+Hr68v8vLy0NHRwefeslwb68JxHJycnNDc3AynLuHlDg4OeOCBB3Dq1CkcPXoUly9fRklJCZYuXQpvb29ER0dj6dKlCA4ONrut20Dpa0FurBCSRqMxWDinpaXx7dQaGhrQ2NgI9+hxZn2+n58f1Go1ysrK0NHRgaKiIhQVFcHV1RV+fn6D+tvU5xnr27PpF1DhERFQKBTgOA42EjU8PDxQW1sLlUrFG+B6FAoFoqKioNVqjRq5xjY49Li5uSEmJqaH8WZjY4MxY8YgPz8fmZmZOHnyJMrLy3H33XebNBB1Oh1c3dxQX18/aF5bS+jtetHj5OSEuLg4VFRUoKCgAO3t7cjIyICLiwvGjBkDHx8fuLm5YeXKlfjqq68Q5utu8j6dlJSEtLQ0xMbGQiKRgOM4xMXFme1h1Z9DjUYDRwcHZGdnIy8vz+QmVXt7O399/Pjjj6isrDQI7/Yyw+i2s7ODRqMxeOgLi+nputB3cHCAo6MjyNPfpNzMzExU9pKLbmtrC6VSCaVSiYiICHh5ecHT0xO2trZIq2zAqZMnjW5udKWwsBBRUVHIy8vjCyZ2Re8xbG9vh52dHVxcXAwiTjQaDWpqagw28vTt9xjigZNIYK9QgBvGEQqBgYGQp+X0ueHT/T6t0+kM5kSVSgV7e3v+mtdvsppDfyNDhGTMmDE4mV2IyspKhAQHA8PAA8/oP8zoFgFFRUV97mK3trYa9EE1l77C0lQqFbKzswel/VZjYyOSk5ORkpLC3+wcHR0xefJkxMfH/7qorW4y2RsyICAAVFGEqqoqfPXVV1iwYAHi480LvxOS7tEK165d4//f3t7e7zBFY+grcXZvGfZbRi6XG90Z5jgO06ZNQ2BgIH744QfU1NTgs88+w/z58xEfHz+oLefMqcifnZ3NG5j6Xf2uocCAZTv6epRKJVxcXBAUFISamhqUlZWhtrYWdXV1ANAZ9WJCf2uGxOvzjNPT08FxHJRKJVpaW6FUKvn36D2xQKeXvry8HMXFxfzx1tZWfPvtt5DJZAgJCUF4ePiNYmiyPjftvL29e71uOI5DWFgYlEoldu/ejZycHHzxxRd47LHHepUns7FBVFQUdFotJBje+a+9XS9d8fb2hoeHB65fv47r16+jvr4en376KeLi4jBz5kx4eHhg5cqV2Jl8EQB6jd5obWtDbXMrysrK+LZh/TG4u57D+hv5ylqtlt+kcnd3R3Nzc68Lbn2uv4uLCwIDAzFixAhcKa8zGUUll8sx6UbxJq1Wy1+L3Rf6bm5uaGhogPZGD/Hm5mZ4ufe94SKVSuHg4MAb2EqlEgqFwiDctvsGWGNjY58GN9BZhPT8+fNwcHCAvb29gXfezs4O8fHxcHJy4j3i+hQCoPMcNjU348jRo/w4hIeHw9/f/6bth36zYs41PtRIJBKzUoy6zzMSiQT29vYGm+UTJ040eM/Vq1fx3Xff9amDsY4NQ0VUVBRO515He3s7Ghoa4ezCrjkxwoxuEWBuQTBHR8d+7TibW/xk69atcHd3R0BAAP/gK8T2Qteq6L0tyPVV0SsqKnDmzBlcuXKFLwzn6emJxMREjB49utdFmKnekLesXo0dO3YgMzMTu3fvRllZGebPn28Vw9YS1Go1fvrpJ5PvGWgOkamc/4FW4rzZjPmuhbXs7Owwbdo0vvBTfn4+mpubkWBGMS9rYU5Ffo1GY7RFlo2NDb9wnjBhApydnQ3y14pVwLlffunTY6BfOHMcBw8PD3h4eKC9vR1lZWUGi+qmpibs3LkT48ePB7n7oaa2VpCaEKY21rq3hFIqlQgNDYVGo4Fare5MQamrQ3FxMZqampCTk4OcnBzs3bsXQROn9unByM/Ph6enp8lrcezYsfD09MSWLVt+9fY1dcrt7Z4nkUoBba8iRYVUKuVbyuk9/xcvXkR6ejqmT5+OiRMnQsJxfLRCQ339r+PRpfhgQEBAvytfm7NJpd+A6l5slOM4ODo6wsnJCaNGjUJAQIBBKGt7VoHZC32pVMqHw3dn9uzZICK0traioaEBV65cMWhX1xuRkZH9LpxprkdOqVSipaUFjY2NUCqVCAoKMri2uxvzesNHpVL1eg4H2g+dweiNPucAC1t06lP4TDmz+irUOdjY2NjA09MT5eXlKK8oZ0a3SGFGtwgwN7fl7rvv7lcbmsLCQuxNSTPrvTU1NaipqUFqaioAwN7eHv7+/rwRPmLECKOFL3rLUQ0LC0NBUzVOnz5tsHgKCgpCYmIiIiIizJrIe+sNaWtri6VLl+LUqVM4cuQIUlJSUFlZiaVLlw5qgTUiQnZ2Nvbs2WMwBnZ2dggJCeFDNIHOTZBr167ByckJdnZ2/SqW0VfO/5IlSyw2gIQq4DdYtLe1IT8/vzNH1EioeGFhIR914OzsjMbGRlRWVuLTTz/F0qVLzWotNhCqqqrMrhLs6ekJNzc3g9zRrhtJxkLAS6ubLPIYAL/+Trty4cIF5ObmIjc3F37jJhvNGbdWe6X+tITS9/3Wt/Ty9PTE1KlTUVlZiZycHOTm5qKoqAhtbW29fJqh/ubk8/n5+eGJJ54w2JzRh153vd5tbWwQ0cUzP5zp63rpjlwuR3R0NEJDQ7F//36Ulpbi0KFDSE5OhiI4CsCN+3QvNTbc3Nz6tRmqUqlQUVFhML4ymQyuLi4Gbc70xraNjQ2cnJz4TShHR0d+w9DY9WLNhb4+SkOpVKKjowP5Zsy3lnjXzP2b8PBwdHR0IDc3Fy0tLUhNTYWfnx9CQkJ6rTPQdTxsPD0Nxlm/OWdpezbG0NDfa3woMeVcsRSJRIJ58+YZXTPpGY6bSd7e3igvL0d1VRUiIiJE7fj4rcKMbhEQGBjY565c94I05so1J2fmueeeQ2lpKYqKinD9+nWUlJSgra2N9x4BnTcxX19fvtpsh7MXGpuaes1RzcjIwLG0FACdC5Po6GgkJibyIYbWQB8+7OPjgx9++AHXr1/H559/jmXLlvEhc0JSUVGB/fv3m906B/g15Nzb2xsjR44E0Ll4PHXqFKRSKWQyGUpLSw1CAgMCAnDq1CmTcnNzc+Hm5tbvm7Q5BfzE2s9SXxVf7z0OCQlBYGAgGhsbkZmZibq6Oqxfvx5z5szBxIkTrToB19XVIS0tDenp6aioqDC7+rCfn59FhV364zXuizFjxkCtVuPixYt9FmkbaB9V4NeWUFWVlT1aQjU1NaG8vNxggdT1OMdx8Pb2hre3N6ZOnYr29nYcSM00a5PDXO9h182xqqoqZGVl9ZSlVg+7Hs/WJjAwEKtXr8alS5dw+PBhNDc3w5xv2nWcdTodOjo6eoRrHz58mA8PV6lUZl8vYWFhFt3rhVjomzvfWuJd695r3ZRsjuPg5uaGvLw8lJeXo7S0FNXV1QgPD4eXl5fR79h988vWxgYajYb15mYMCr05VwZCdHQ0li1bJlh0oBC43Ihic3Z2hk6nY0a3CGFGtwgwZ1du3rx5/b4Azc2Z0Rdu6cyH7MyZKy8v5/P5rl+/jqamJpSUlKCkpARnz56F16h4mLM8iY+PR2JioqA3uIiICDz22GP4z3/+g+rqaj7Pe/z48YJ8XktLC44dO4aUlBQQEaRSKaKjo5GW1reXQ6lUguM4g3wkjUbDV6HWaDQ9eg7rwwVNoVKpcPLkSXh6eiImJoZ//cqVK5BKpZBKpbh48aJBjp+Li0ufBfz27duH++67b9jtCPeFTqdDVlYWXxU/MjKS92g7OTkhPj4era2tyM7Oxt69e1FYWIg77rhjQJ+pUqlQVVWFqqoqnD17ln9dIpHA3c0NDY2NJo3YgYa7mTQk+uGpcnV1xaxZsxASEoIDqaZzyIWsANvc3IzLly9Do9FALpebteloZ2cHDw8Ps4zu/nocdTodcnP6Lkw50E2I4QzHcRg3bhxiYmKwa9cuVPb9JygtLUVxcTFUKlWvGx053cZVKpFA2y1s3BgON/p3W4K1F/qW5qiaQ9caCObIlslkiIqKgre3N3JyctDa2oqMjAxcvnwZCxYsMPq9u25+6XPeWZcNhpiJjo5GVFSU0dS57uusYcGN+6u1a6gwBg9mdIuEvnblLA3zNSuUrhtSqRQjRozAiBEjMGXKFP4GcP36dd4bToBZhV1GjRo1KDuK7u7uWH0jzzsrKwu7du1CWVkZ5s2bZ7U8b61Wi3PnzuH48eP8WEZHR2P27NlwdnbusyCeXC5HfHx8j5unra0tEhISoNFooNVqER8fb1BMq7m52aCFjCm6bszodDqDas/di3D5+/v3aczrCxQNh7Ya/SE/Px9VVVWQSCQYOXIk3/JIj0wmw/Lly3H27FkcPHgQmZmZKCsrw5RFS+Ho5Gj2pKdWq3lDu76+nn+d4ziEhIQgNjYW0dHRKGjR9FncyxrhbtY0JPTtWPoiIyODD4t3cXGxyvXW0tKC7BsGt5OTU78iZPrjFewPRUVFJgtwAcOrDY2Q2NraIi4uDvsvZvT53oYbBdD0SCSSHi249DmY+loFuY0qnD17VhCvsZAIlaNqlmwjERYuLi6Ij4/H9evXce3aNeTk5OCjjz7CjBkzMHnyZJMb+d2LuzEYYkQikfQrLXOoMdVWVAzpS791mNEtIkztyg2EgYbS6RfyLi4uGD16NADgSFoOsrOz+/xbc4vEWQO5XI5ly5bhxIkTOHbsGM6fP8/neQ/EI6IPUz5w4ADfhsXHxwdz5841uJkPJIfI1taW97x1z0MsLCzE6dOn+9QzNjbWYBGqD+vXe9Dd3NwMwjrN7SVcW1sLJyenYRnq1FsP38DAQDQ0NCA0NLTXfu4cx2HKlCkICAjAtm3bUF9fj4upF+Hl5YX6+vpeJz2NptOArqqqQl1dnUE7ImdnZ3h5eWHBggWGv7kW0xX5+xsCPhiYWxtBrVajtLQUpaWlkEgkcHZ2hpubG9zd3c1qx9b9HOp0Oly+dAmOajUcHR1NFls0Rn+9guZi7r1sOLWhEZLuLbR6Q18JXW9kGzPkut/zOK5DMK+x0AgRum6W7F6MeYlEgqCgIHh6eqK5uRlFRUU4cOAArly5gkWLFgE2nfeprtchwLzcDMZg01db0Zs5felmgRndIkOoXTlrh9KZ6lnblcEsagZ0fs9bbrkFvr6+2L59O4qKivDZZ59h+fLlFuWTV1ZW4sCBA8jLywPQGR4+c+ZMjBs3rocR2jVaoampie8vO9AcInNy/uVyeY/K6BzHGVTJNWbMm9OC6vr16yAihIWFWaC9cOh3hDtUKkgkElRXV8P2hgHr7qowu43ciBEj8MQTT2DXrl2oJjIadqaf9BwdHWHXWGVQMdnR0ZHvqasvuNXbJo+1QsAHA3NzVGNDQlBfX4+amhq0t7ejrq4OdXV1aG1tRWRkJIDOqAuNRtMjpNvYOQQ6o2gcHBwwZswYszeHuiLEBoe597Lh1IbGGPoxGKgX09zx8PHxGXCdgo4bfdp1Ot2w3aTqihA5qgOVrVAo8PDDD+PChQs4dOgQysrK8PnnnyNu7h1wdHREfn4+fx3qdDpUV1fD3d2deddEiLWuccbgodPp+uzYcLOnL90MMKNbBHRtv9Ub+vZbwwVzQjgtKf5mLSIjI7F69Wps2bIF1dXV2LBhAxYuXAh5QHiffxvj4YjW1lbeW67P2548eTKmT59uss2UEDlEWbUtiJ9/Vx9en5B+34jNMeb1Hviu4dmNjY2dxcG8vAYU3mnOeBirPgz03BHW53/qjWP3kSH9Wiza2dnh7rvvxpe7D5rMu25qaoKtTgelUskb2uZ4c7si5ILcmpibo+rhqoCHh0dnX+bWVtTV1aG2thbu7u78+0pKSrBx40YEBQV1LgZHhKKlpQUZGb+GJ3fP4R0xYoRFBrcea29wCFkoazDhJBLYWqFV3mCMh5g2qcQAx3GIj49HVFQU9u3bh/T0dBQXFxu8R38dajQa5l0TKda6xsWKOWtq9z7fMbgUFRX12Vb0t5K+JGaY0c0QBHNCOC0p/mZNPDw8sHr1avz444/Izs7Gzp07MW7OIoSHhQEcZ3QhRzodkpOTcfz4cb7V18iRIzF79myzPdUSiQQeHh4oLy9HSEiIVcZACM+dOQX87rrrrh7fu6Kigg8nlsvl8PT0hEajgZ+fn1HD39o9wLv38JVKJFAolWhtaeEXjJb0RL9+/Xqf1boBICoq6jdT1be/Oar6nsbdo0qKioqg1WqRn5+P/Pz8HoUYjZ3DwsJCeHt7D7gyurUWKEIWyhpMOjo6UFlZCS8vrwF55QdrPDiOg0KhQHNzM1y69P9mWI6DgwOWLFmC0aNH42haDl+fpbd7KfOuiQtrXeOMwYOlL90c3DRG94cffoh33nkH5eXlGDt2LN5//31MmjRpqNX6TdPXgjx6ZIiJvx4c5HI5li9fjuPHj+P48eMoLS1FQ3091BqNwc1LLpfD29sb1dXVKDx3EkCnl3Xu3Lk9+hibg1qtRlFREaKioqz2XYTw+phTwK+7R9rT05MPP1SpVCguLsbBgwfh4uKCUaNGYfr06XxYm6ke4OZuYuhzpvWLvsrKSoPfGyeRwE4u7+zP3MXj3d8dYXMnveGY2y4k1shRTUxMRFRUFN9Pu4XjDHLhrXUOhUbIQlmDhU6rRWNDAzzcB+7rGazxsKbOjF+Ry+UGBVHFch0yTMOuF/Fxs6QvCcG5c+dw+vRpNDc3w8fHB/Pnz7dq+2FrclMY3Vu2bMEf/vAHfPLJJ5g8eTL+9a9/Ye7cucjOzjbIWWUMPkIWjbEWHMfh1ltvhY+PD45n5qPFSFVmlUqFoqIiAJ3eupkzZyIuLm7YGVhC9bPsTwE/fVG9iIgI1NXVobKyErm5uaivr0daWhpmzpwJoO8e4EuWLOFDwDUaDZqbm/mK7fpibz/88AMaGhowb948xMXFATC/qnZ/d4TZpNc7A/3dcRwHDw8PeHh4ICEhwexCjMNxV18M97zBhI2HeGHeNQZjeHCzpC9Zm7S0NBw4cAALFy6Ev78/kpOTsWnTJjz11FNQKpVDrV4Pbgqj+3//93/x2GOPYdWqVQCATz75BP/973/x5Zdf4sUXXxxi7RhiyVGNjIzEz1evocNE/1epVIq1a9dCofht5bBZUsBPIpHA3d0d7u7uSEpKwtWrV6HT6cBxHHQ6Hfbu3Wvy769evcqHgDc1NeHy5cs93qPvs9217ZC5lej7axyzSW/wMLcQ43Dd4BDLPW+wYOMhTthGI4MxPLhZ0pesTXJyMsaPH887XW6//Xbk5OTg4sWLmDp16hBr1xPRG90dHR1ISUnBSy+9xL8mkUgwa9YsnDlzxujf6L1kevQL9rKyMmGVtZCaZtO9XwGguL2hz/cMpmwx6nz9+nW+5ZcpLl26hICAgH7L74q+mnNJSQnfWqd7v9reUHfrBWzOeJAZIZzd5ZqLOXqr1WreGC0uLsb169d7FOjpTk1NDQoKCuDk5IT29nZoNBrI5XK+eJutrS1GjBgBR0dHODk58fI4AB1drnGZTAYJgKbGRj4n20GmhVqt5qth96Zzd9zd3Ex6YP2iosz6DQ3mORSjbI1aLdg5FON4DKbOqrZ2NNTXo7a6BnJ7O6vKNoY1xkMMOgspWyi5Eomkz+tQLpf3eR0aky2k3mI8h0LKZtfLzfH74AD4+fqisLCwR7pOcHAwOKDP67A32cMBvQ3W0NBg0JJQLpcbLVCs1WpRWlpqYFxzHIfQ0NA+15dDheiN7urqami12h5VjL29vZGVlWX0b/7xj3/gtdde6/E6ywFnMBgMBoPBYDAYjMFn1KhRBs9fffVV/O1vf+vxvtbWVhBRjzBypVJp1ubDUCB6o9sSXnrpJfzhD3/gn2s0GmRmZiIgIGDY5egao6mpCTExMcjIyLBqn2uh5IpVthh1FlK2GHUWUrYYdRZSthh1FlK2GHUWUjbTWfyyxaizkLLFqLOQssWos5CyxajzUKLvZBMTE2PQDtRUG16xIXqj28PDA1KptEcF5YqKil7b9hgLVUhKShJMR2ujr/Q8YsQIgxCM4SpXrLLFqLOQssWos5CyxaizkLLFqLOQssWos5Cymc7ily1GnYWULUadhZQtRp2FlC1GnYeawMBAs9+rUCjAcRyfoqmnpaXF7No+g83wd+v2ga2tLeLj43H48GH+NZ1Oh8OHDyMhIWEINWMwGAwGg8FgMBgMhjWRSqXw8/NDfn4+/xoRIT8/H/7+/kOoWe+I3tMNAH/4wx/w0EMPYcKECZg0aRL+9a9/oaWlha9mzmAwGAwGg8FgMBiMm4MpU6Zgx44d8PPzw4gRI5CcnAy1Wo1x48YNtWpGuSmM7uXLl6OqqgqvvPIKysvLMW7cOOzbt69HcbWbBblcjldffdXqeQ5CyRWrbDHqLKRsMeospGwx6iykbDHqLKRsMeospGyms/hli1FnIWWLUWchZYtRZyFli1FnsTFq1Ci0trbi2LFjaG5uho+PD+6///5hG17OERENtRIMBoPBYDAYDAaDwWDcjIg+p5vBYDAYDAaDwWAwGIzhCjO6GQwGg8FgMBgMBoPBEAhmdDMYDAaDwWAwGAwGgyEQzOhmMBgMBoPBYDAYDAZDIJjRzWAwGAwGg8FgMBgMhkAwo5vBYDD6iUqlgkqlGmo1flMcO3YMbW1tQ60Gg8FgMLrB5sTBh82J4oMZ3QwGg2EGBw8exIIFC+Dq6gqFQgGFQgFXV1csWLAAhw4dGmr1euXSpUv4+9//jo8++gjV1dUGxxobG/HII49YJPeLL77AQw89hA0bNgAAtmzZgujoaISGhuLVV18dsN7dmTNnDgoLCwcko7Ky0uB5amoqHnroISQlJWHJkiU4duzYgOR3RaVSIS8vb8AL0dGjR+P//b//h+vXr1tJs76pqKhAeXm5VWRptVpUVFSgqqrKKvK60tDQgOzsbGRnZ6OhocHq8sUMEUGr1Vpd7ldffSW6sc7JycHhw4eRm5s71KqYpPv5OnfuHJKTk61mzBYVFeHs2bP45ZdfUFNTM2B5bE40RIxzImOQIYYoKCsrox07dtAnn3xCn3zyCe3YsYPKysoE/czm5mY6fvy4oJ9hKRqNxuB5cnIyHT9+nDo6Oqz+WQ8//DCVlJRYVWZHRwddvXqV6uvrrSq3rq6OPvvsM/rLX/5Cn3/++YDknz9/3oqaGVJRUUGHDx/m9SsvL6d//vOf9I9//IMuX748YPl5eXm0ceNGeuutt+jtt9+mbdu2UUNDg8XyvvrqK5LJZHTvvffShg0baM+ePbRnzx7asGED3XfffWRjY0Nff/31gPU2RkZGBoWEhFj0t/v37ydbW1uKjY2lwMBAcnd3pyNHjvDHy8vLSSKR9Fvu//3f/5FSqaS7776bfH196e9//zu5u7vT3//+d3rttdfIycmJPv30U4t0jouLM/rgOI6io6P555YgkUiooqKCiIh+/vlnsrGxoVtuuYWef/55mj17NslkMovueRs2bKDTp08TEVFbWxs98sgjJJVKSSKRkEwmoyeeeILa29st0pnjOHJ3dyepVEpz586lbdu2kVqttkhWd2pqauiee+6hgIAAWrNmDWk0Gnr00UeJ4ziSSCSUkJBApaWlFsnevXs3TZs2jeRyOUkkEpJIJOTs7EwPPPAAXbt2bUB6f/755xQdHc3L1T+io6Ppiy++GJDs3khNTbXoWtHz3//+lx599FF6/vnnKTMz0+BYbW0tzZgxwyK5arWaXn75ZZo+fTq98sorRET09ttvk0KhIFtbW1q5ciWpVCqL9e6OjY0NZWRkDEjG2bNnDebwXbt20fTp08nPz4/i4+Np48aNFst+88036dChQ0TUOa633XYbcRzH/6bnzZtHdXV1/Zbr4OBAjzzyCP38888W69YbhYWFFB8fT1KplObNm0cNDQ00a9YsXu/Q0FDKzs62WP6HH35IgYGBPa6XpKQki+d5NicaItY5kTG4MKN7mNPc3Ez3338/SaVSkslk5OXlRV5eXiSTyUgqldIDDzxALS0tgny2pYuMjo4Oev755yksLIwmTpxI69evNzhu6U2NiKi0tJSSkpJIKpXS9OnTqba2lhYuXMhPTpGRkRYvEi9dumT0YWNjQz/++CP/vL/885//pNbWViLq3Cz44x//SLa2tvyCfNWqVRZvFixevJi+//57IiJKS0sjDw8P8vT0pMmTJ5O3tzf5+PhYvEDiOI7CwsLojTfesOqmw9GjR0mpVBLHceTj40Opqank7+9PERERFBUVRXK5nPbv32+R7ObmZlqyZInBIsvHx4ekUik5ODjQBx98YJHciIgIk3/74YcfUnh4uEWy+2Igi/2EhAT685//TEREOp2O/vnPf5KDgwPt3buXiCy/FkeOHEmbN28mIqILFy6QTCYzMHa++OILio+Pt0hnmUxG8+bNo7/97W/849VXXyWJREJPPvkk/5olcBzHG92zZ8+mRx55xOD4unXraObMmf2WGxISQsnJyURE9Nxzz1FwcDBt376dMjMzaceOHRQZGUnPP/+8xTqXlJTQjz/+SIsWLSKZTEaenp70xz/+ccDGzyOPPEKjRo2i999/n2655Ra68847acyYMXTq1Ck6ffo0TZw4kVauXNlvuV9//TU5OjrSH//4R3r55ZfJx8eHXnzxRfr444/plltuIQ8PD7p69apFOusNyhdffJGOHj1KGRkZlJGRQUePHqWXXnqJlEolvfPOOxbJNkVqaipxHGfR327evJmkUiktXLiQpk6dSnZ2drRp0yb++EDmxL/85S/k7e1Nf/jDHygmJobWrFlDAQEBtGnTJtq4cSONGDGC/vnPf/Zbrqurq9EHx3Hk7OzMP7eErptfO3fuJIlEQitXrqQPP/yQVq9eTTKZjLZv326RbH9/f7pw4QIREa1evZri4uLowoUL1NbWRqmpqTRlyhR69NFH+y2X4ziKjY0ljuNo5MiR9D//8z9UWVlpkY7dueeee+iWW26hXbt20bJlyygpKYluvfVWKi4uptLSUpo7dy7dddddFsl+5513yM/Pj95//31+s+r111+nvXv30oMPPkgKhYJ++eWXfstlc6IhYp0TGYMLM7qHOY8++ihFRETQvn37DHaGNRoN7d+/nyIjI2n16tWCfLalN7ZXX32VvL296Z133qGXX36ZnJ2d6fHHH+ePl5eXW7x4efDBBykxMZF27txJy5cvp8TERJo2bRoVFxfTtWvXKCkpidauXWuRbL2RpjfYuj70r1syHl0XGO+88w65urrSl19+Senp6bRp0yby8vKyaFFE1Lkw0ntN5s+fTytWrOC9Gh0dHfToo4/SnDlzLJLNcRw99thj/CbPwoUL6ccff+wRZdBfpk6dSmvXrqWmpiZ65513aMSIEQbn7LnnnqPExESLZD/++OOUlJREV65coZycHFqyZAm98MIL1NLSQuvXryeFQsFPjP1BLpdTVlZWr8ezsrLIzs7OIp2fffZZk48HHnjA4gWGk5MT5ebmGry2efNmUiqVtGvXLosXGPb29gbeSrlcTmlpafzznJwccnFxsUjnU6dOUVhYGL3yyiuk1Wr512UyGaWnp1skU09Xo9vX15fOnDljcFy/cdVf5HI5Px6RkZH8Ak7P8ePHKTAwcMA6E3VuPL755psUERHBe6O7b2yai6+vL++509+XDxw4wB8/deoUjRgxot9yR44cSf/5z3/457/88gv5+/uTTqcjIqLly5fT4sWLLdI5MDCQtmzZ0uvx//znPxQQENBvuYsXLzb5mDlzpsXX4bhx4+i9997jn2/ZsoWUSiW/KB+I0R0aGkq7du0ios7rTiKRGIz9li1baNSoUf2W6+DgQAsXLqSvvvqKf2zYsIGkUim98cYb/GuW0PU3PXXqVHrxxRcNjr/xxhs0ZcoUi2TL5XIqLCwkIqLg4OAekSvnz58nX19fi3VOTU2lp556itzc3MjW1pbuvvtu2rNnD//btgRPT0+6ePEiERHV19cTx3F08uRJ/nhKSgp5e3tbJDs4OJj27NnDP8/OziZ3d3c+Wubpp5+m2bNn91sumxMNEeucyBhcmNE9zHFxcTEZznTq1CmLL+TedrL1DycnJ4tuPuHh4fwigKjzZhMeHk4PP/ww6XS6AS0wui6Ua2pqiOM4PpSMiOjw4cMUGhpqkeyxY8fSwoULKTMzkwoLC6mwsJAKCgpIJpPRwYMH+df6S9cFRlxcXI8Qo02bNlFsbKxFOtvb2/MTiK+vL7/Dryc7O5ucnZ0tkq3XW61W07Zt22jBggUklUrJ29ubXnjhBYvD3bpOemq1mmQyGb/gICK6evWqxTp7eHgYhMvV1taSnZ0dHw3ywQcf0Lhx4/otd/z48SY9lS+88AKNHz++/wpT56bM+PHj6dZbbzX6mDBhgsXXi6enp9Hwwe+++44UCgV9/PHHFsl2d3c38LL6+/sbXBs5OTnk4OBgkc5EnQvPe++9lyZPnsz/VqxldOfm5lJDQwOFhIT0uF5yc3NJoVD0W25QUBAfojhixIgenqOMjAxSKpUW6dx10647R48epQceeMBi2QqFwuC82djY0JUrV/jn+fn5Fsm2t7engoICg9dkMhkfMXP27FmL5y07OzuTHv709HSyt7fvt1yZTEbz58+nhx9+2OjjjjvusPg6VCqVlJ+fb/DakSNHyMHBgT7++OMBzYl2dnZUVFRk8Lxr+Hp+fj45Ojr2W25OTg4f6dDU1MS/bu3NLy8vrx73qKysLIt/H5GRkbR7924i6oxA6b5+unjxIjk5OQ1IZyKi9vZ2+vbbb+m2224jiURC/v7+9Ne//tUinR0dHfnfh1arJZlMRqmpqfzxnJwci84hUec13vVa1Ol0JJPJ+IjA1NRUi+7VbE40RKxzImNwYUb3MMfJyclk6M+5c+csmkCIOm/Gf/zjHw12srs+XnvtNYt3/LovuIqLiykyMpLuv/9+KikpsdoCQ6lUUk5ODv/82rVrFi24iIhUKhWtW7eOYmJiDBbjA72xcRzHh6G5u7sbLGqJOhdFliz0iYgmT55Mn332GRF1GvQ//vijwfEDBw6Qj4+PRbK7LzKIOs/j66+/TqGhoSSRSGjatGn9luvh4cHvALe0tJBEIjHwOF66dMkibyNR5yZV17DVjo4Okslk/PhfvXrVot13fUj86NGj6dlnn6W33nqL3nrrLXr22WdpzJgx5ODgYHH9g8jISPrmm296PX7x4kWLr5fZs2f3Gmr77bffko2NjUWyk5KSDLxp3dm1a5dF3rXufPnll+Tj40Offvop2djYWGWxr89n5DiOv3b0/PTTTxaFRP75z3+mhIQEqquroxdffJEWLVrEGyotLS20bNmyAUWc9GZ067G0XsHYsWP5ENE9e/aQo6Mjvfvuu/zxjz/+2KLzGB0dzae9EHV66mxtbfkomZycHIs3CqZNm0YrV640mteu0Who5cqVNH369H7LHT16tMl88IFch8aiKoiIjh07Rg4ODvTyyy9bLNvb29ugDkZiYiIVFxfzzzMzMy1eI6jVanrhhRcoLCyMTp06RUTWM7qPHj1Kly5doqCgIDp37pzB8aysLIsNlHfeeYeio6MpJyeH3n33XUpISOCNlPz8fLr11ltpyZIl/ZZravOroKCA/vKXv1gUYUFENGXKFPrLX/5CRJ33PG9vbwPv/+uvv25xaPK4ceMM7nOHDx8mhULBe+azsrIsMujZnGiIWOdExuDCjO5hzooVK/icpO5cuHCB4uPj6f7777dIdmJiIv3rX//q9bil4eUhISEG3mc9JSUlFBkZSbNnz7b4hhkYGEhnz57ln//pT3+impoaA50tNdj07Nmzh/z9/enNN9/kd50HanS/8cYb9N5775Gvr2+PiejSpUsW58bt3r2b3NzcaMOGDbRhwwYKDg6mL774gn7++Wf68ssvKSAgwOJcUlOLDCKiQ4cO0YoVK/ot984776Tbb7+dTp06RY8//jhNmDCBFi5cSM3NzdTS0kJLliyhefPmWaTz7NmzDULV33nnHYNQwgsXLlj8+ygoKKAXXniBpk+fTpGRkRQZGUnTp0+nP/3pTz02mfrDihUr6Jlnnun1+EBySbdv325S9ubNm+nWW2/tt9xTp04ZRCd058MPP6T333+/33KNcfXqVZo4cSJxHDfgBcaxY8cMHt2jNf71r3/R22+/3W+5KpWK7rjjDnJ1daXZs2eTnZ0dKRQKioiIIKVSSYGBgRZHhjz88MPU2Nho0d/2xaZNm0gqlVJ4eDjJ5XL6/vvvyc/Pj5YtW0b33nsv2draWlQH4YMPPiBnZ2d64YUX6JVXXiE/Pz+DPNpNmzZZXPjn0qVL5OPjQ+7u7rR48WJas2YNrVmzhhYvXkzu7u7k6+vbY2PTHB5++GF68sknez2ekZFBwcHBFul855138kXOuqM3XiydE2fMmGEyzHvr1q0WG2x6Dh8+TIGBgfTSSy9ZdfNLn771f//3fwbHv/vuO4qJibFY/u9//3uysbGhkSNHkp2dHUkkEr6OyoQJEywqQmvO5pelIeb79u0jOzs7srW1JTs7Ozp+/DhFRkbSpEmTaMqUKSSVSk2mVJhiy5YtZGNjQ8uWLaOVK1eSg4ODgUH/ySefUEJCgkWy2Zz4K2KdExmDC0dENNQV1Bm9U1dXhxUrVmD//v1wdXWFl5cXgM7WN/X19Zg7dy6+/fZbuLi49Fv2m2++CbVa3Wsrg+vXr+OVV17h2x+Yy+rVq0FEWL9+fY9jJSUluPXWW5Gfn29RO5M777wTM2fOxLp164we//DDD7F9+3YcPny437K7UlFRgVWrVqG5uRlnzpzBpUuXEBMTY5Gs4OBgcBzHP1+3bh2eeeYZ/vl7772H//znPzhz5oxF8n/44Qc888wzKC0tRdfLWS6XY82aNfif//kfSKXSfsuVSCQoLy/nf3PWIicnBwsXLkRubi5GjhyJgwcP4sknn8SePXsAAK6urti3bx/Gjx/fb9kXLlzA7NmzYWtrC1tbW5SXl2Pjxo249957AXT+Ps6dO4eNGzda9TsNhPLycqhUKgQFBQ21KsMWnU6HpqYmODk5GVxLw419+/Zh165dyM/Ph06ng6+vL5KSkrBixQoolcqhVs8oP//8M5KTk5GQkIDExERkZGTgrbfeQmtrKxYtWoSHHnrIIrkff/wxNm3aBJVKhblz5+Kvf/0r7OzsAHTeA7RaLUaOHGmR7KamJmzatAnJycl8azMfHx8kJCRgxYoVcHJy6rdMlUoFrVYLhUJhkU6mOH78OE6fPo2XXnrJ6PGjR4/i66+/7vdcCwBXr16FjY0NQkJCjB7/9ttvIZPJsGzZsn7L7kpNTQ0ee+wxHD16FMnJyYiKirJY1rVr1wyeOzg4wN3dnX/+9ddfAwBWrlxp8WdkZmZi9+7dPa7FWbNmWXQPee211/D8888L8vsAgMLCQqSkpCA+Ph7BwcGoqKjAhx9+iNbWVixcuBAzZsywWPbevXsNrsXHHnuMP6ZvHdZ1/IcaNif2jVjmRIYhzOgWCZmZmUYXGJYuWoTk2rVryMrKwty5c40eLy0txcGDBy1ezJni3LlzUCgUGDVqlFXk/fvf/8bRo0fx/vvvw9/f3yoyu5OcnAy5XI64uDiLZWi1WqSkpKCgoIBfYMTHx8PR0dFimcePH0dSUhJkMpnFMkxRU1NjMNEfPnwYbW1tSEhIGNACoKysDLt374ZKpcLMmTMt3iwxhkajQXp6On8d+vr6Ijo6GjY2Nlb7DCHorrePjw9iYmIGrLdQcsUsWyjEqDODwWAwGIwbDKWbncFgMMSAVqull19+mVxcXHpUtndxcaG//OUvBlVFLUGtVlNqairt27eP9u3bR6mpqQPuOy+U3kKOh1hlE4nrHAqtt5ByTdHR0THgPuDGUKvVgsgVq2wx6kzEfh/WlP3hhx/SbbfdRkuXLu2RUlhVVWVxL22hEUpvIcdDrGPNMEQYFxbD6hw5cgSnTp1CWVkZJBIJQkNDcccddyAiImLYyhajzsZkh4WFYdGiRWw8fsO/jxdffBFfffUV3nrrLcydOxfe3t4AOtMQDhw4gL/+9a/o6OjAP//5z37L1ul0eOWVV/Dhhx+ioaHB4JizszOeeuopvPbaa5BIJMNGbyHHQ4yyxXgOhdRbyPHoi4yMDIwfP96i9CVTpKenCyJXrLLFqDPAfh/Wkv3vf/8bL730ElatWoWGhgYsWLAAf/vb3/gUCq1W2yONoD989NFH2L59O9zc3PDEE0/gtttu449VV1dj0qRJyM/P77dcofQWcjyEHmvGIDLUVj/DNBUVFTRp0iSSSCQkk8lIIpFQfHw8+fj4kFQqtbhIlpCyxaizkLLFqLOQssWos7e3N+3bt6/X4/v27SMvLy+LZD///PPk6elJn3zyCRUUFFBrayu1trZSQUEBffrpp+Tl5UUvvPDCsNJbyPEQo2wxnkMh9RZyPPrC0gKgQyVXrLLFqLOQssWo80Bkx8TE0ObNm/nnP//8M3l6evJt0wbSBu+9994jhUJBa9eupQceeIBsbW3pzTff5I8PRLZQegs5HkLKZgwuzNM9zHn66afh5+eHuro6yOVyPPfcc2hsbMT58+dx5MgRLFu2DCNGjOi1sNhQyBajzkLKFqPOQsoWo85NTU3w8/Pr9bivry9aWlr6rS/QWTTom2++6VEDITg4GI8//jiCgoKwcuVKizyZQukt5HiIUbYYzyEgnN5CjkdfBRbb2tr6LVNIuWKVLUadhZQtRp2FlF1QUIDExET+eWJiIo4cOYJZs2ZBrVYbFIvtL59++ik+//xzrFixAgDwu9/9DnfddRfa2trw+uuvWyxXSL2FHA8hZTMGF1ZIbZjj7OyM06dPIzY2FgDQ0tICV1dXVFdXw8nJCZs2bcLf//53ZGVlDRvZYtRZSNli1FlI2WLUeeHChdBoNNi8eTM8PDwMjlVXV+PBBx+EVCrF7t27+62zUqlEcnIyRo8ebfT45cuXkZiYiObm5n7LFkpvIcdDjLLFeA6F1FvI8bCzs8O9997ba7XusrIyfP755/0OlxVKrlhli1FnIWWLUWchZQcGBmLz5s2YNm2awesZGRmYOXMm5s6di02bNlmks0KhQEZGBoKDg/nX0tLSMGvWLKxatQrPPPMM/Pz8LJItlN5CjoeQshmDzFC72hmm8fT0NOjD19raShKJhO9NnZeXR3K5fFjJFqPOQsoWo85CyhajzkVFRTRq1CiSyWQUFxdH8+bNo3nz5lFcXBzJZDIaM2YMFRUVWaTzggULaM6cOVRVVdXjWFVVFc2bN48WLlxokWyh9BZyPMQoW4znUEi9hRyP+Ph4+uijj3o9fvHiRYtCLYWSK1bZYtRZSNli1FlI2ffdd1+v/a7T0tLI09PTYp0DAgLoxIkTPV5PT08nb29vWrlypcWyhdJbyPEQUjZjcLF+FROGVZk6dSpeeeUVtLS0QK1W489//jNCQ0Ph5uYGAKiqqoKrq+uwki1GnYWULUadhZQtRp0DAgJw6dIl7Ny5E4sWLUJgYCACAwOxaNEi7Nq1CxcvXkRAQIBFOn/yyScoLS2Fr68vxo8fj/nz52P+/PkYP348fH19UVpaio8//tgi2ULpLeR4iFG2GM+hkHoLOR5JSUnIzs7u9bijoyOmT58+bOSKVbYYdRZSthh1FlL2iy++iDFjxhg9FhsbiyNHjuCVV17pt1ygcx7fvn17j9djYmJw+PBh7N271yK5gHB6CzkeQspmDC4svHyYk5+fjzlz5uDatWvgOA5KpRLff/89Zs2aBQD46quvkJ2djX/84x/DRrYYdRZSthh1FlK2GHUWGp1Oh/379yM5OdmgD3NCQgLmzJkjSJVnhnUR6zkUSm+xjgeDwRhaLl++jJSUFKxatcro8bS0NPzwww949dVXB1kzBmNgMKNbBLS2tuLUqVPo6OjAlClTeuT1DUfZYtRZSNli1FlI2WLUGQDOnTuHM2fOGBgRiYmJmDhxotU+QwiE0lvI8RCrbKEQo84MBuPmxth9KSEhAZMmTRpizUwjlN5CjodYx5rxK8zoZjAYjD6orKzEPffcg59//hmBgYEGvZKLioqQlJSEH374AV5eXhZ/hhBGlVB6CzkeYpUNiOscCq23kHJ7k/1bXjSz8Rgc2WLUWQjZlZWVuPvuu3H69OlBvS8NdDyE0lvI8RiMsWYMEkOZUM4wj9bWVlq/fj2tWrWK5s2bRwsWLKCnnnqKDh06NGxli1FnIWWLUWchZYtN53vuuYcSEhIoKyurx7GsrCxKTEykJUuWWCS7oqKCpk6dShzHUVBQEE2aNIkmTZpEQUFBxHEcTZ06lSoqKoaV3kKOhxhli/EcCqm3kOPBdB4c2WLUWUjZYtRZSNlC35eSkpJEdT8V47zFGHyY0T3MycnJoaCgIPLy8qKAgADiOI4WLlxIkydPJqlUSkuXLiW1Wj2sZItRZyFli1FnIWWLUWcHBwe6cOFCr8fPnz9PDg4OFuks5IQqlN5CjocYZYvxHBKxBajYdRZSthh1FlK2GHUWUrYY70tEbE5kDC3M6B7mzJ8/n5544gnS6XRERPTWW2/R/PnziYjo6tWrFBwcTK+++uqwki1GnYWULUadhZQtRp3d3d3p2LFjvR4/evQoubu7W6SzkBOqUHoLOR5ilC3Gc0jEFqCDIVesssWos5CyxaizkLLFeF8iYnMiY2hhRvcwR6FQ0NWrV/nnKpWKbGxsqLq6moiIduzYQcHBwcNKthh1FlK2GHUWUrYYdX7yyScpKCiItm/fTg0NDfzrDQ0NtH37dgoODqannnrKIp2FnFCF0lvI8RCjbDGeQyK2AB0MuWKVLUadhZQtRp2FlC3G+xIRmxMZQwszuoc5fn5+lJKSwj+vq6sjjuOosbGRiIjy8/NJLpcPK9li1FlI2WLUWUjZYtS5vb2d1qxZQ7a2tiSRSMjOzo7s7OxIIpGQra0t/e53v6P29naLdBZyQhVKbyHHQ4yyxXgOhdRbjAtQMeospGwx6iykbDHqLKRsMd6XhNRbjPMWY/BhRvcw56GHHqJbbrmFMjMzKT8/n5YvX05xcXH88WPHjlFAQMCwki1GnYWULUadhZQtRp31NDQ00JEjR+jbb7+lb7/9lo4cOWKwKLCEwZhQhdBbSLliky3Wc8gWoOLWWUjZYtRZSNli1Flo2UTiui8JrbeQcoWWzRgcWMuwYU5lZSXuvPNOnD17FhzHISAgAD/++CPi4uIAANu2bUNZWRl+//vfDxvZYtRZSNli1FlI2WLUeTBobGxESkqKQXuU+Ph4ODk5DbFmDHMR6zkUSm8hx4PpPDiyxaizkLLFqLPQsoVCjDozGKZgRrdIyMnJgUqlwsiRIyGTyUQhW4w6CylbjDoLKVtsOre1tSElJQVubm6IiYkxONbe3o6tW7di5cqVVvksayKU3kKOh1hlC4UYdWYwGDc3Yr0vsTmRMWQMraOdMVCKiopo1apVopItRp2FlC1GnYWUPRx1zs7O5nuESiQSmj59OpWUlPDHy8vLSSKRWKxXa2srnTx5ktLT03sca2tro40bN1okVyi9hRwPscoW2zkUWm+h5AopW4w6CylbjDoLKVuMOgslW6z3JTYnMoYSZnSLnNTUVMEuNqFki1FnIWWLUWchZQ9Hne+66y5auHAhVVVVUU5ODi1cuJBCQkLo2rVrRDR8J1Sh9BZyPMQoW4znUEi9xbgAFaPOQsoWo85CyhajzkLKFuN9SUi9xThvMQYf68Z0MqzOzp07TR7Pz88fdrLFqLOQssWos5Cyxajz6dOncejQIXh4eMDDwwO7du3Ck08+iWnTpuHo0aNQKpUWyQWAP/3pTxg1ahTOnz+P+vp6PPPMM5g6dSqOHTuGwMBAi+UKqbeQ4yFG2WI8h0LqLeR4MJ0HR7YYdRZSthh1FlK2GO9LQuotxnmLMQQMtdXPMI1+p4/juF4flu5wCSVbjDoLKVuMOgspW4w6Ozo6UkZGRo/X165dS/7+/nTixAmLdfby8qLLly/zz3U6Ha1Zs4YCAwMpLy9vQLvYQukt5HiIUbYYz6GQegs5HkznwZEtRp2FlC1GnYWULcb7kpB6i3HeYgw+zOge5vj5+dGOHTt6PX7x4kWLLzahZItRZyFli1FnIWWLUeeJEyfS119/bfTY2rVrycXFZVhOqELpLeR4iFG2GM8hEVuAil1nIWWLUWchZYtRZyFli/G+RMTmRMbQIhlqTzvDNPHx8UhJSen1OMdxIAsL0AslW4w6CylbjDoLKVuMOi9evBjfffed0WMffPAB7rvvPot1HjlyJM6fP29U7p133ok77rjDIrmAcHoLOR5ilC3GcwgIp7eQ48F0HhzZYtRZSNli1FlI2WK8LwFsTmQMMYNr4zP6y4kTJ2jv3r29Hm9ubqZjx44NK9li1FlI2WLUWUjZYtRZSN58802aP39+r8d/97vfEcdxg6gRo7+I9RwKpbeQ48F0HhzZYtRZSNli1Flo2UIhRp0ZDHNgfboZDAaDwWAwGAwGg8EQCBZezmAwGAwGg8FgMBgMhkAwo5vBYDAYDAaDwWAwGAyBYEY3g8FgMBgMBoPBYDAYAsGMbgaDwWAwGAwGg8FgMASCGd0MBoPBYDAYDAaDwWAIBDO6GQwGg8FgMBgMBoPBEAhmdDMYDAaDwWAwGAwGgyEQ/x/KGJv+ng4wlgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "# Load the asylum data and prediction data\n",
        "asylum_path = '/content/gdrive/MyDrive/TimePressure/asylum_year.xlsx'\n",
        "asylum_df = pd.read_excel(asylum_path)\n",
        "\n",
        "# Rename and scale the asylum data for visibility\n",
        "asylum_df.rename(columns={'asylum_nr_year': 'asylum_total'}, inplace=True)\n",
        "asylum_df['asylum_total_scaled'] = asylum_df['asylum_total'] / 10000\n",
        "\n",
        "# Assuming prediction_df is already loaded and contains 'date', 'tp_l_sentence_pred_first_non_negative', and 'portfolio' columns\n",
        "prediction_df['date'] = pd.to_datetime(prediction_df['date'])\n",
        "prediction_df['year'] = prediction_df['date'].dt.year\n",
        "\n",
        "# Calculate positive sentences per year for 'President' portfolio\n",
        "president_df = prediction_df[prediction_df['portfolio'] == 'President']\n",
        "positive_sentences_president = president_df.groupby('year')['tp_l_sentence_pred_first_non_negative'].sum()\n",
        "unique_speech_ids_president = president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Calculate positive sentences per year for non-'President' portfolios\n",
        "non_president_df = prediction_df[prediction_df['portfolio'] != 'President']\n",
        "positive_sentences_non_president = non_president_df.groupby('year')['tp_l_sentence_pred_first_non_negative'].sum()\n",
        "unique_speech_ids_non_president = non_president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Calculate combined positive sentences per year\n",
        "combined_sentences_per_year = positive_sentences_president + positive_sentences_non_president\n",
        "\n",
        "# Plot the results\n",
        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "\n",
        "\n",
        "# Plot combined line\n",
        "ax1.plot(combined_sentences_per_year.index, combined_sentences_per_year.values, marker='o', color='black', linestyle='-', label='Implicit Low Time Pressure Invocations')\n",
        "\n",
        "# Plot scaled asylum numbers on the same axis\n",
        "ax1.plot(asylum_df['year'], asylum_df['asylum_total_scaled'], color='darkgrey', marker='s', linestyle='-', label='Yearly European Asylum Numbers (10,000)')\n",
        "ax1.set_ylim(0, 400)\n",
        "ax1.set_ylabel('Number of Implicit Low Time Pressure Invocations', color='black')\n",
        "\n",
        "# Bar plot for unique speech_id for both 'President' and non-'President' portfolios\n",
        "ax2 = ax1.twinx()\n",
        "ax2.bar(unique_speech_ids_president.index, unique_speech_ids_president.values, width=0.4, align='center', color='lightblue', alpha=0.5, label='Speeches President')\n",
        "ax2.bar(unique_speech_ids_non_president.index + 0.4, unique_speech_ids_non_president.values, width=0.4, align='center', color='lightgrey', alpha=0.5, label='Speeches Commissioner')\n",
        "ax2.set_ylabel('Number of speeches', color='gray')\n",
        "ax2.tick_params(axis='y', labelcolor='gray')\n",
        "\n",
        "# Add vertical lines with Commissioner tenure labels and position labels lower\n",
        "events = {\n",
        "    1995: 'Gradin',\n",
        "    1999: 'Vitorino',\n",
        "    2004: 'Fratini',\n",
        "    2008: 'Barrot',\n",
        "    2010: 'Malmström',\n",
        "    2014: 'Avramopoulos',\n",
        "    2019: 'Johansson'\n",
        "}\n",
        "\n",
        "for year, name in events.items():\n",
        "    ax1.axvline(x=year, color='darkgrey', linestyle='--', lw=1)\n",
        "    ax1.text(year, ax1.get_ylim()[0] + 200, name, color='darkgrey', rotation=90, verticalalignment='bottom', horizontalalignment='center')\n",
        "\n",
        "# Set x-axis to show every year and rotate labels vertically\n",
        "ax1.set_xticks(range(prediction_df['year'].min(), prediction_df['year'].max() + 1))\n",
        "ax1.set_xticklabels(range(prediction_df['year'].min(), prediction_df['year'].max() + 1), rotation=90)\n",
        "\n",
        "# Add legend\n",
        "lines, labels = ax1.get_legend_handles_labels()\n",
        "lines2, labels2 = ax2.get_legend_handles_labels()\n",
        "ax1.legend(lines + lines2, labels + labels2, loc='upper left')\n",
        "\n",
        "# Adjust layout\n",
        "fig.tight_layout()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "6jdHEhx7qJ6w",
        "outputId": "62a23277-02b3-4495-e8b2-3c60e38c3ae0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "# Assuming prediction_df is already loaded and contains 'date', 'tp_h_sentence_pred', and 'portfolio' columns\n",
        "\n",
        "# Ensure the date column is in datetime format\n",
        "prediction_df['date'] = pd.to_datetime(prediction_df['date'])\n",
        "\n",
        "# Extract the year from the date column\n",
        "prediction_df['year'] = prediction_df['date'].dt.year\n",
        "\n",
        "# Calculate positive sentences per year for 'President' portfolio\n",
        "president_df = prediction_df[prediction_df['portfolio'] == 'President']\n",
        "positive_sentences_president = president_df.groupby('year')['tp_l_sentence_pred'].sum()\n",
        "unique_speech_ids_president = president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Calculate positive sentences per year for non-'President' portfolios\n",
        "non_president_df = prediction_df[prediction_df['portfolio'] != 'President']\n",
        "positive_sentences_non_president = non_president_df.groupby('year')['tp_l_sentence_pred'].sum()\n",
        "unique_speech_ids_non_president = non_president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Calculate combined positive sentences per year\n",
        "combined_sentences_per_year = positive_sentences_president + positive_sentences_non_president\n",
        "\n",
        "# Plot the results\n",
        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "# Plot line for 'President' portfolio\n",
        "ax1.plot(positive_sentences_president.index, positive_sentences_president.values, marker='o', color='grey', linestyle='-', label='President')\n",
        "ax1.set_ylabel('Number of Implicit Ease of Time Pressure Invocations', color='black')\n",
        "ax1.tick_params(axis='y', labelcolor='blue')\n",
        "\n",
        "# Plot line for non-'President' portfolios\n",
        "ax1.plot(positive_sentences_non_president.index, positive_sentences_non_president.values, marker='o', color='grey', linestyle='--', label='Commissioner')\n",
        "ax1.tick_params(axis='y', labelcolor='black')\n",
        "\n",
        "# Plot combined line\n",
        "ax1.plot(combined_sentences_per_year.index, combined_sentences_per_year.values, marker='o', color='black', linestyle='-', label='Combined')\n",
        "\n",
        "# Set y-axis limits for ax1\n",
        "ax1.set_ylim(0, 250)\n",
        "\n",
        "# Bar plot for unique speech_id for both 'President' and non-'President' portfolios\n",
        "ax2 = ax1.twinx()\n",
        "ax2.bar(unique_speech_ids_president.index, unique_speech_ids_president.values, width=0.4, align='center', color='lightblue', alpha=0.5, label='Speeches President')\n",
        "ax2.bar(unique_speech_ids_non_president.index + 0.4, unique_speech_ids_non_president.values, width=0.4, align='center', color='lightgrey', alpha=0.5, label='Speeches Commissioner')\n",
        "ax2.set_ylabel('Number of speeches', color='gray')\n",
        "ax2.tick_params(axis='y', labelcolor='gray')\n",
        "\n",
        "# Set x-axis to show every year and rotate labels vertically\n",
        "ax1.set_xticks(range(prediction_df['year'].min(), prediction_df['year'].max() + 1))\n",
        "ax1.set_xticklabels(range(prediction_df['year'].min(), prediction_df['year'].max() + 1), rotation=90)\n",
        "\n",
        "# Add legend\n",
        "lines, labels = ax1.get_legend_handles_labels()\n",
        "lines2, labels2 = ax2.get_legend_handles_labels()\n",
        "ax1.legend(lines + lines2, labels + labels2, loc='upper left')\n",
        "\n",
        "# Adjust layout\n",
        "fig.tight_layout()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "q6iEiY_K8gA3",
        "outputId": "84d0a088-3863-4a4c-8e69-9018891059d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "# Assuming prediction_df is already loaded and contains 'date', 'tp_l_sentence_pred', and 'portfolio' columns\n",
        "\n",
        "# Ensure the date column is in datetime format\n",
        "prediction_df['date'] = pd.to_datetime(prediction_df['date'])\n",
        "\n",
        "# Extract the year from the date column\n",
        "prediction_df['year'] = prediction_df['date'].dt.year\n",
        "\n",
        "# Calculate positive sentences per year for 'President' portfolio\n",
        "president_df = prediction_df[prediction_df['portfolio'] == 'President']\n",
        "positive_sentences_president = president_df.groupby('year')['tp_l_sentence_pred'].sum()\n",
        "unique_speech_ids_president = president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Calculate positive sentences per year for non-'President' portfolios\n",
        "non_president_df = prediction_df[prediction_df['portfolio'] != 'President']\n",
        "positive_sentences_non_president = non_president_df.groupby('year')['tp_l_sentence_pred'].sum()\n",
        "unique_speech_ids_non_president = non_president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Calculate combined positive sentences per year\n",
        "combined_sentences_per_year = positive_sentences_president + positive_sentences_non_president\n",
        "\n",
        "# Plot the results\n",
        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "# Plot line for 'President' portfolio\n",
        "ax1.plot(positive_sentences_president.index, positive_sentences_president.values, marker='o', color='grey', linestyle='-', label='President')\n",
        "ax1.set_ylabel('Number of Implicit Ease of Time Pressure Invocations', color='black')\n",
        "ax1.tick_params(axis='y', labelcolor='blue')\n",
        "\n",
        "# Plot line for non-'President' portfolios\n",
        "ax1.plot(positive_sentences_non_president.index, positive_sentences_non_president.values, marker='o', color='grey', linestyle='--', label='Commissioner')\n",
        "ax1.tick_params(axis='y', labelcolor='black')\n",
        "\n",
        "# Plot combined line\n",
        "ax1.plot(combined_sentences_per_year.index, combined_sentences_per_year.values, marker='o', color='black', linestyle='-', label='Combined')\n",
        "\n",
        "# Set y-axis limits for ax1\n",
        "ax1.set_ylim(0, 550)\n",
        "\n",
        "# Bar plot for unique speech_id for both 'President' and non-'President' portfolios\n",
        "ax2 = ax1.twinx()\n",
        "ax2.bar(unique_speech_ids_president.index, unique_speech_ids_president.values, width=0.4, align='center', color='lightblue', alpha=0.5, label='Speeches President')\n",
        "ax2.bar(unique_speech_ids_non_president.index + 0.4, unique_speech_ids_non_president.values, width=0.4, align='center', color='lightgrey', alpha=0.5, label='Speeches Commissioner')\n",
        "ax2.set_ylabel('Number of speeches', color='gray')\n",
        "ax2.tick_params(axis='y', labelcolor='gray')\n",
        "\n",
        "# Add vertical lines with Commissioner tenure labels and move them further below\n",
        "events = {\n",
        "    '1995': 'Gradin',\n",
        "    '1999': 'Vitorino',\n",
        "    '2004': 'Fratini',\n",
        "    '2008': 'Barrot',\n",
        "    '2010': 'Malmström',\n",
        "    '2014': 'Avramopoulos',\n",
        "    '2019': 'Johansson'\n",
        "}\n",
        "\n",
        "# Plot vertical lines for each event and move the labels lower to avoid overlap with the legend\n",
        "for year, name in events.items():\n",
        "    ax1.axvline(x=int(year), color='darkgrey', linestyle='--', lw=1)\n",
        "    ax1.text(int(year), ax1.get_ylim()[0] + 350, name, color='darkgrey', rotation=90, verticalalignment='bottom', horizontalalignment='center')\n",
        "\n",
        "# Set x-axis to show every year and rotate labels vertically\n",
        "ax1.set_xticks(range(prediction_df['year'].min(), prediction_df['year'].max() + 1))\n",
        "ax1.set_xticklabels(range(prediction_df['year'].min(), prediction_df['year'].max() + 1), rotation=90)\n",
        "\n",
        "# Add legend\n",
        "lines, labels = ax1.get_legend_handles_labels()\n",
        "lines2, labels2 = ax2.get_legend_handles_labels()\n",
        "ax1.legend(lines + lines2, labels + labels2, loc='upper left')\n",
        "\n",
        "# Adjust layout\n",
        "fig.tight_layout()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "aJXe-x8Y5oDM",
        "outputId": "b8f29356-6bda-4d93-958f-b7dc2a1b9fc9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 11) Same token level analyis for explicit low time pressure"
      ],
      "metadata": {
        "id": "2e60YxIVj0jZ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "outputId": "c9a32f9e-75b5-410e-8c17-28ff89677826",
        "id": "WgsSoCzDkRQS"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   ID                                           Sentence  \\\n",
              "0   1  Mr, chairman, ladies, gentlemen and dear friends!   \n",
              "1   1  As the Commission member responsible for justi...   \n",
              "2   1  I am convinced that without a credible and vis...   \n",
              "3   1  With such a policy, we have a good chance to b...   \n",
              "4   1  From my perspective, the European integration ...   \n",
              "\n",
              "                                     Sentence Before  \\\n",
              "0                                                NaN   \n",
              "1  Mr, chairman, ladies, gentlemen and dear friends!   \n",
              "2  As the Commission member responsible for justi...   \n",
              "3  I am convinced that without a credible and vis...   \n",
              "4  With such a policy, we have a good chance to b...   \n",
              "\n",
              "                                      Sentence After manual_check  \\\n",
              "0  As the Commission member responsible for justi...          NaN   \n",
              "1  I am convinced that without a credible and vis...          NaN   \n",
              "2  With such a policy, we have a good chance to b...          NaN   \n",
              "3  From my perspective, the European integration ...          NaN   \n",
              "4  What would be the main features of a credible ...          NaN   \n",
              "\n",
              "        speaker    year  length topic harmonized_topic  tp_h  resp_f  tp_l  \\\n",
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              "summary": "{\n  \"name\": \"result_df\",\n  \"rows\": 5273,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20,\n        \"min\": 1,\n        \"max\": 68,\n        \"num_unique_values\": 68,\n        \"samples\": [\n          47,\n          17,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sentence\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5145,\n        \"samples\": [\n          \"[s] It is time for the EU and its Member States to act firmly  against it [resp_f].\",\n          \"The United States, Canada and Australia are more successful in attracting skilled migrants, than Europe.\",\n          \"But if it does lead to democratisation and a greater emphasis on the fair rule of law, it will definitely have an impact on the chances for refugees coming to these countries.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sentence Before\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3931,\n        \"samples\": [\n          \"For example through the development and implementation of a comprehensive Human Resources and Training Strategy.\",\n          \"[s] Migration can bring great benefits [stance_p\",\n          \"I want the EU's activities in this field to feed into the work of the Global Counter-Terrorism Forum (GCTF) which the US has proposed to launch in 2011.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sentence After\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3586,\n        \"samples\": [\n          \"I will tell you about a young man called Tusse.\",\n          \"The topics on today's 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\"samples\": [\n          \"franco frattini\",\n          \"dimitris avramopoulos\",\n          \"Anita Gradin\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.603468443048055,\n        \"min\": 1995.0,\n        \"max\": 2023.0,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          2005.0,\n          2014.0,\n          1995.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 911.0269822294068,\n        \"min\": 395.0,\n        \"max\": 3737.0,\n        \"num_unique_values\": 54,\n        \"samples\": [\n          1202.0,\n          1022.0,\n          663.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n  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            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "# Specify the path within your Google Drive where the Excel file is located\n",
        "excel_file_path = '/content/gdrive/MyDrive/TimePressure/multilabel_gold_all_final.xlsx'\n",
        "\n",
        "\n",
        "# Load the Excel file into a DataFrame\n",
        "result_df = pd.read_excel(excel_file_path)\n",
        "\n",
        "# Display the DataFrame\n",
        "result_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_q4BrH2rkAil"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "import pandas as pd\n",
        "import re\n",
        "\n",
        "# Assuming result_df is already read from the Excel file\n",
        "\n",
        "# Iterate through each row of the DataFrame\n",
        "for index, row in result_df.iterrows():\n",
        "    # Check if tp_h label is not equal to 1\n",
        "    if row['resp_s'] != 1:\n",
        "        # Convert the Sentence column to a string (if not already)\n",
        "        sentence = str(row['Sentence'])\n",
        "        # Remove square brackets and their contents from the Sentence column\n",
        "        sentence = re.sub(r'\\[[^\\]]*\\]', '', sentence)\n",
        "        # Update the Sentence column with the preprocessed sentence\n",
        "        result_df.at[index, 'Sentence'] = sentence\n",
        "\n",
        "# Save the preprocessed DataFrame to a new Excel file\n",
        "result_df.to_excel('preprocessed_data.xlsx', index=False)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XEHJ9KDGkbec"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import re\n",
        "from collections import defaultdict\n",
        "\n",
        "# Assuming result_df is already read from the Excel file\n",
        "\n",
        "# Initialize lists to store token-level data\n",
        "token_list = []\n",
        "label_list = []\n",
        "\n",
        "# Define a function to split a sentence into tokens\n",
        "def tokenize(sentence):\n",
        "    # Split the sentence into tokens (you may need to customize this based on your tokenization requirements)\n",
        "    return sentence.split()\n",
        "\n",
        "# Iterate through each row of the DataFrame\n",
        "for _, row in result_df.iterrows():\n",
        "    # Split the sentence into tokens\n",
        "    tokens = tokenize(row['Sentence'])\n",
        "\n",
        "    # Initialize a dictionary to store token-level labels\n",
        "    token_labels = defaultdict(int)\n",
        "\n",
        "    # Find the start and end indices of [s] and [tp_h] markers\n",
        "    start_index = row['Sentence'].find('[s]')\n",
        "    end_index = row['Sentence'].find('[resp_s]')\n",
        "\n",
        "    # Check if both start and end markers are found\n",
        "    if start_index != -1 and end_index != -1:\n",
        "        # Extract the token span between start and end indices\n",
        "        token_span = tokenize(row['Sentence'][start_index+len('[s]'):end_index])\n",
        "\n",
        "        # Assign label 1 to tokens within the span, 0 otherwise\n",
        "        for token in token_span:\n",
        "            token_labels[token] = 1\n",
        "\n",
        "    # Append token-label pairs to the lists\n",
        "    for token in tokens:\n",
        "        token_list.append(token)\n",
        "        label_list.append(token_labels[token])\n",
        "\n",
        "# Create a new DataFrame to store token-level data\n",
        "token_df = pd.DataFrame({'Token': token_list, 'Label': label_list})\n",
        "\n",
        "# Save the token-level DataFrame to a new Excel file\n",
        "token_df.to_excel('token_level_data.xlsx', index=False)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "outputId": "0854b2ec-45bc-4bcb-d8c2-c6aa6bac35dc",
        "id": "FLgWO9HskpM1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tokenized Texts: tensor([[  101,  2720,  1010,   102,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0],\n",
            "        [  101,  3472,  1010,   102,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0],\n",
            "        [  101,  6456,  1010,   102,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0],\n",
            "        [  101, 11218,   102,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0],\n",
            "        [  101,  1998,   102,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0]])\n",
            "Labels: tensor([0, 0, 0, 0, 0])\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import torch\n",
        "from transformers import BertTokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "# Load the token-level DataFrame\n",
        "token_df = pd.read_excel('token_level_data.xlsx')\n",
        "\n",
        "# Tokenizer\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "\n",
        "# Initialize lists to store token-level data and labels\n",
        "tokenized_texts = []\n",
        "labels = []\n",
        "\n",
        "# Iterate through each row of the token-level DataFrame\n",
        "for _, row in token_df.iterrows():\n",
        "    # Tokenize the token using the BERT tokenizer\n",
        "    tokens = tokenizer.tokenize(row['Token'])\n",
        "\n",
        "    # Add special tokens [CLS] and [SEP]\n",
        "    tokens = ['[CLS]'] + tokens + ['[SEP]']\n",
        "\n",
        "    # Add token IDs to the list of token-level data\n",
        "    token_ids = tokenizer.convert_tokens_to_ids(tokens)\n",
        "    tokenized_texts.append(token_ids)\n",
        "\n",
        "    # Add labels to the list of labels\n",
        "    labels.append(row['Label'])\n",
        "\n",
        "# Pad sequences to the maximum length\n",
        "max_len = max(len(seq) for seq in tokenized_texts)\n",
        "input_ids = pad_sequences(tokenized_texts, maxlen=max_len, dtype=\"long\", value=0, truncating=\"post\", padding=\"post\")\n",
        "\n",
        "# Convert lists to PyTorch tensors\n",
        "input_ids = torch.tensor(input_ids)\n",
        "labels = torch.tensor(labels)\n",
        "\n",
        "# Print the first few tokenized texts and labels\n",
        "print(\"Tokenized Texts:\", input_ids[:5])\n",
        "print(\"Labels:\", labels[:5])\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "New context approach"
      ],
      "metadata": {
        "id": "pdNRqFAelTF3"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tcSCUU5VlS4m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import re\n",
        "from transformers import BertTokenizer\n",
        "\n",
        "# Load the data\n",
        "excel_file_path = '/content/gdrive/MyDrive/TimePressure/multilabel_gold_all_final.xlsx'\n",
        "result_df = pd.read_excel(excel_file_path)\n",
        "\n",
        "# Clean and ensure string format for sentence columns\n",
        "result_df['Sentence'] = result_df['Sentence'].fillna('').astype(str)\n",
        "result_df['Sentence Before'] = result_df['Sentence Before'].fillna('').astype(str)\n",
        "result_df['Sentence After'] = result_df['Sentence After'].fillna('').astype(str)\n",
        "\n",
        "# Initialize tokenizer\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "\n",
        "# Define a function to clean sentences by removing annotation labels\n",
        "def clean_sentence(sentence):\n",
        "    return re.sub(r'\\[.*?\\]', '', sentence).strip()\n",
        "\n",
        "# Prepare a list for tokenized data with labels\n",
        "token_data = []\n",
        "\n",
        "# Iterate to create token-level data\n",
        "for index, row in result_df.iterrows():\n",
        "    # Clean sentences\n",
        "    cleaned_sentence = clean_sentence(row['Sentence'])\n",
        "    sentence_before = clean_sentence(row['Sentence Before'])\n",
        "    sentence_after = clean_sentence(row['Sentence After'])\n",
        "\n",
        "    # Concatenate with context\n",
        "    combined_sentence = f\"{sentence_before} {cleaned_sentence} {sentence_after}\"\n",
        "    tokens = tokenizer.tokenize(combined_sentence)\n",
        "\n",
        "    # Tokenize each part individually for easier label assignment\n",
        "    tokens_before = tokenizer.tokenize(sentence_before)\n",
        "    tokens_main = tokenizer.tokenize(cleaned_sentence)\n",
        "    tokens_after = tokenizer.tokenize(sentence_after)\n",
        "\n",
        "    # Create labels array\n",
        "    labels = [-100] * len(tokens_before)  # Context before tokens\n",
        "    for token in tokens_main:\n",
        "        # Check if the main sentence has specific annotations like [resp_f]\n",
        "        if '[resp_f]' in row['Sentence']:\n",
        "            labels.append(1)  # Label for 'resp_f'\n",
        "        else:\n",
        "            labels.append(0)  # Default or other labels\n",
        "    labels.extend([-100] * len(tokens_after))  # Context after tokens\n",
        "\n",
        "    # Append the tokenized data with context\n",
        "    token_data.append({\n",
        "        'Sentence Before': sentence_before,\n",
        "        'Sentence': cleaned_sentence,\n",
        "        'Sentence After': sentence_after,\n",
        "        'tokens': tokens,\n",
        "        'labels': labels\n",
        "    })\n",
        "\n",
        "# Convert the list to a DataFrame\n",
        "token_level_df = pd.DataFrame(token_data)\n",
        "\n",
        "# Save the token-level data with context to an Excel file\n",
        "token_level_data_path = '/content/gdrive/MyDrive/TimePressure/token_level_data_with_context.xlsx'\n",
        "token_level_df.to_excel(token_level_data_path, index=False)\n",
        "\n",
        "# Display the token-level DataFrame\n",
        "print(token_level_df.head())\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nLepzeCflSlk",
        "outputId": "979ad3a4-370a-4bdb-d986-04b9429c12a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                     Sentence Before  \\\n",
            "0                                                      \n",
            "1  Mr, chairman, ladies, gentlemen and dear friends!   \n",
            "2  As the Commission member responsible for justi...   \n",
            "3  I am convinced that without a credible and vis...   \n",
            "4  With such a policy, we have a good chance to b...   \n",
            "\n",
            "                                            Sentence  \\\n",
            "0  Mr, chairman, ladies, gentlemen and dear friends!   \n",
            "1  As the Commission member responsible for justi...   \n",
            "2  I am convinced that without a credible and vis...   \n",
            "3  With such a policy, we have a good chance to b...   \n",
            "4  From my perspective, the European integration ...   \n",
            "\n",
            "                                      Sentence After  \\\n",
            "0  As the Commission member responsible for justi...   \n",
            "1  I am convinced that without a credible and vis...   \n",
            "2  With such a policy, we have a good chance to b...   \n",
            "3  From my perspective, the European integration ...   \n",
            "4  What would be the main features of a credible ...   \n",
            "\n",
            "                                              tokens  \\\n",
            "0  [mr, ,, chairman, ,, ladies, ,, gentlemen, and...   \n",
            "1  [mr, ,, chairman, ,, ladies, ,, gentlemen, and...   \n",
            "2  [as, the, commission, member, responsible, for...   \n",
            "3  [i, am, convinced, that, without, a, credible,...   \n",
            "4  [with, such, a, policy, ,, we, have, a, good, ...   \n",
            "\n",
            "                                              labels  \n",
            "0  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -100, -100, ...  \n",
            "1  [-100, -100, -100, -100, -100, -100, -100, -10...  \n",
            "2  [-100, -100, -100, -100, -100, -100, -100, -10...  \n",
            "3  [-100, -100, -100, -100, -100, -100, -100, -10...  \n",
            "4  [-100, -100, -100, -100, -100, -100, -100, -10...  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XfWoMHd23vcr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "9.1.2 Run BERT model explicit low invocations"
      ],
      "metadata": {
        "id": "boouRNVthNf0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from transformers import BertTokenizer, BertForTokenClassification, AdamW, get_linear_schedule_with_warmup\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score\n",
        "from tqdm import tqdm\n",
        "from sklearn.model_selection import train_test_split\n",
        "import numpy as np\n",
        "\n",
        "# Load the token-level DataFrame\n",
        "token_df = pd.read_excel('token_level_data.xlsx')\n",
        "\n",
        "# Tokenizer\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "\n",
        "# Define your token-level dataset\n",
        "class TokenDataset(Dataset):\n",
        "    def __init__(self, tokenizer, token_df, max_length):\n",
        "        self.tokenizer = tokenizer\n",
        "        self.token_df = token_df\n",
        "        self.max_length = max_length\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.token_df)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        tokens = self.tokenizer.tokenize(self.token_df.loc[idx, 'Token'])\n",
        "        tokens = ['[CLS]'] + tokens + ['[SEP]']\n",
        "\n",
        "        input_ids = self.tokenizer.convert_tokens_to_ids(tokens)\n",
        "        input_ids = input_ids[:self.max_length] + [0] * (self.max_length - len(input_ids))\n",
        "\n",
        "        attention_mask = [1] * len(tokens)\n",
        "        attention_mask = attention_mask[:self.max_length] + [0] * (self.max_length - len(attention_mask))\n",
        "\n",
        "        label = self.token_df.loc[idx, 'Label']\n",
        "        label = [label] + [-100] * (self.max_length - 1)  # Pad labels with -100 for ignored tokens\n",
        "\n",
        "        return {\n",
        "            'input_ids': torch.tensor(input_ids),\n",
        "            'attention_mask': torch.tensor(attention_mask),\n",
        "            'labels': torch.tensor(label)\n",
        "        }\n",
        "\n",
        "# Custom collate function to handle padding\n",
        "def custom_collate_fn(batch):\n",
        "    input_ids = torch.stack([item['input_ids'] for item in batch])\n",
        "    attention_masks = torch.stack([item['attention_mask'] for item in batch])\n",
        "    labels = torch.stack([item['labels'] for item in batch])\n",
        "\n",
        "    return input_ids, attention_masks, labels\n",
        "\n",
        "# Calculate class weights\n",
        "class_counts = token_df['Label'].value_counts().to_dict()\n",
        "total_samples = len(token_df)\n",
        "class_weights = {cls: total_samples/count for cls, count in class_counts.items()}\n",
        "\n",
        "weights = [class_weights[cls] for cls in sorted(class_weights.keys())]\n",
        "weights = torch.tensor(weights, dtype=torch.float)\n",
        "\n",
        "# Define your training function\n",
        "def train_model(model, train_loader, val_loader, criterion, optimizer, scheduler, epochs):\n",
        "    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "    model.to(device)\n",
        "    criterion = criterion.to(device)\n",
        "\n",
        "    best_val_f1 = 0\n",
        "\n",
        "    for epoch in range(epochs):\n",
        "        model.train()\n",
        "        total_loss = 0.0\n",
        "\n",
        "        for batch in tqdm(train_loader, desc=f\"Training Epoch {epoch+1}/{epochs}\"):\n",
        "            input_ids, attention_masks, labels = batch\n",
        "            input_ids = input_ids.to(device)\n",
        "            attention_masks = attention_masks.to(device)\n",
        "            labels = labels.to(device)\n",
        "\n",
        "            optimizer.zero_grad()\n",
        "            outputs = model(input_ids=input_ids, attention_mask=attention_masks)\n",
        "            logits = outputs.logits\n",
        "\n",
        "            logits = logits.view(-1, model.config.num_labels)\n",
        "            labels = labels.view(-1)\n",
        "\n",
        "            loss = criterion(logits, labels)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            scheduler.step()\n",
        "\n",
        "            total_loss += loss.item()\n",
        "\n",
        "        avg_loss = total_loss / len(train_loader)\n",
        "        val_precision, val_recall, val_f1 = evaluate_model(model, val_loader, criterion)\n",
        "        print(f\"Epoch {epoch+1}/{epochs}, Loss: {avg_loss}, Validation Precision: {val_precision}, Validation Recall: {val_recall}, Validation F1 Score: {val_f1}\")\n",
        "\n",
        "        # Save model if it has improved\n",
        "        if val_f1 > best_val_f1:\n",
        "            best_val_f1 = val_f1\n",
        "            torch.save(model.state_dict(), 'best_model.pt')\n",
        "\n",
        "def evaluate_model(model, val_loader, criterion):\n",
        "    model.eval()\n",
        "    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "    all_preds = []\n",
        "    all_labels = []\n",
        "    total_loss = 0.0\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for batch in tqdm(val_loader, desc=\"Evaluating\"):\n",
        "            input_ids, attention_masks, labels = batch\n",
        "            input_ids = input_ids.to(device)\n",
        "            attention_masks = attention_masks.to(device)\n",
        "            labels = labels.to(device)\n",
        "\n",
        "            outputs = model(input_ids=input_ids, attention_mask=attention_masks)\n",
        "            logits = outputs.logits\n",
        "\n",
        "            logits = logits.view(-1, model.config.num_labels)\n",
        "            labels = labels.view(-1)\n",
        "\n",
        "            loss = criterion(logits, labels)\n",
        "            total_loss += loss.item()\n",
        "\n",
        "            preds = logits.argmax(dim=-1).cpu().numpy().flatten()\n",
        "            label_ids = labels.cpu().numpy().flatten()\n",
        "\n",
        "            mask = label_ids != -100  # Ignore padded labels\n",
        "            all_preds.extend(preds[mask].tolist())\n",
        "            all_labels.extend(label_ids[mask].tolist())\n",
        "\n",
        "    avg_loss = total_loss / len(val_loader)\n",
        "    precision = precision_score(all_labels, all_preds, average='weighted')\n",
        "    recall = recall_score(all_labels, all_preds, average='weighted')\n",
        "    f1 = f1_score(all_labels, all_preds, average='weighted')\n",
        "    return precision, recall, f1\n",
        "\n",
        "# Split the dataset\n",
        "train_df, temp_df = train_test_split(token_df, test_size=0.3, random_state=42)\n",
        "val_df, test_df = train_test_split(temp_df, test_size=0.5, random_state=42)\n",
        "\n",
        "# Reset indices after splitting\n",
        "train_df = train_df.reset_index(drop=True)\n",
        "val_df = val_df.reset_index(drop=True)\n",
        "test_df = test_df.reset_index(drop=True)\n",
        "\n",
        "# Token-level datasets\n",
        "max_length = 128\n",
        "train_dataset = TokenDataset(tokenizer, train_df, max_length=max_length)\n",
        "val_dataset = TokenDataset(tokenizer, val_df, max_length=max_length)\n",
        "test_dataset = TokenDataset(tokenizer, test_df, max_length=max_length)\n",
        "\n",
        "# DataLoaders for training, validation, and testing\n",
        "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, collate_fn=custom_collate_fn)\n",
        "val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False, collate_fn=custom_collate_fn)\n",
        "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False, collate_fn=custom_collate_fn)\n",
        "\n",
        "# BERT model\n",
        "model = BertForTokenClassification.from_pretrained('bert-base-uncased', num_labels=2)\n",
        "\n",
        "# Define criterion and optimizer\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "criterion = nn.CrossEntropyLoss(weight=weights.to(device), ignore_index=-100)  # Use class weights\n",
        "optimizer = AdamW(model.parameters(), lr=2e-5)\n",
        "\n",
        "# Learning rate scheduler\n",
        "total_steps = len(train_loader) * 3  # Assuming 3 epochs\n",
        "scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=0, num_training_steps=total_steps)\n",
        "\n",
        "# Train the model\n",
        "train_model(model, train_loader, val_loader, criterion, optimizer, scheduler, epochs=3)\n",
        "\n",
        "# Evaluate on the test set\n",
        "test_precision, test_recall, test_f1 = evaluate_model(model, test_loader, criterion)\n",
        "print(f\"Test Precision: {test_precision}, Test Recall: {test_recall}, Test F1 Score: {test_f1}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2eb90a23-3737-49ff-a983-a8ac8d905a58",
        "id": "TF5bovIn3xQc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n",
            "Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:591: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n",
            "Training Epoch 1/3: 100%|██████████| 2200/2200 [05:49<00:00,  6.30it/s]\n",
            "Evaluating: 100%|██████████| 472/472 [00:26<00:00, 18.15it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3, Loss: 0.6400233187526464, Validation Precision: 0.9408177083951099, Validation Recall: 0.7972148541114058, Validation F1 Score: 0.8581115944169111\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training Epoch 2/3: 100%|██████████| 2200/2200 [05:48<00:00,  6.31it/s]\n",
            "Evaluating: 100%|██████████| 472/472 [00:26<00:00, 18.13it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 2/3, Loss: 0.5813896617361091, Validation Precision: 0.9417935368828326, Validation Recall: 0.8456896551724138, Validation F1 Score: 0.8876592761314844\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training Epoch 3/3: 100%|██████████| 2200/2200 [05:48<00:00,  6.31it/s]\n",
            "Evaluating: 100%|██████████| 472/472 [00:26<00:00, 18.09it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 3/3, Loss: 0.5319019168412144, Validation Precision: 0.9429427389739564, Validation Recall: 0.8499336870026525, Validation F1 Score: 0.890403258835302\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evaluating: 100%|██████████| 472/472 [00:25<00:00, 18.16it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Precision: 0.9392170888233804, Test Recall: 0.8474137931034482, Test F1 Score: 0.887253323045398\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Store the model"
      ],
      "metadata": {
        "id": "8G4iJRRxk_K7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# After training is complete, save the model\n",
        "model.save_pretrained('/content/gdrive/MyDrive/TimePressure/resp_srs_model/')\n",
        "\n",
        "# Save the tokenizer\n",
        "tokenizer.save_pretrained('/content/gdrive/MyDrive/TimePressure/resp_srs_model/')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c25bf445-c593-4764-d953-fad68e47998d",
        "id": "UsM-VGPAlCOx"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('/content/gdrive/MyDrive/TimePressure/resp_srs_model/tokenizer_config.json',\n",
              " '/content/gdrive/MyDrive/TimePressure/resp_srs_model/special_tokens_map.json',\n",
              " '/content/gdrive/MyDrive/TimePressure/resp_srs_model/vocab.txt',\n",
              " '/content/gdrive/MyDrive/TimePressure/resp_srs_model/added_tokens.json')"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 11.2.1 Prediction in President and Home Affairs speeches"
      ],
      "metadata": {
        "id": "iBgqqipBhBQO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import torch\n",
        "from transformers import BertTokenizer, BertForTokenClassification\n",
        "from tqdm import tqdm\n",
        "\n",
        "# Define paths\n",
        "model_path = '/content/gdrive/MyDrive/TimePressure/resp_srs_model/'\n",
        "prediction_data_path = '/content/gdrive/MyDrive/TimePressure/migration_sample_combined2.feather'\n",
        "\n",
        "# Load the trained model\n",
        "model = BertForTokenClassification.from_pretrained(model_path)\n",
        "model.eval()\n",
        "\n",
        "# Load the tokenizer\n",
        "tokenizer = BertTokenizer.from_pretrained(model_path)\n",
        "\n",
        "# Load the new data\n",
        "prediction_df = pd.read_feather(prediction_data_path)\n",
        "\n",
        "# Define a function to prepare the new data\n",
        "def prepare_data(sentences, tokenizer, max_length):\n",
        "    input_ids = []\n",
        "    attention_masks = []\n",
        "\n",
        "    for sentence in sentences:\n",
        "        tokens = tokenizer.tokenize(sentence)\n",
        "        tokens = ['[CLS]'] + tokens + ['[SEP]']\n",
        "\n",
        "        ids = tokenizer.convert_tokens_to_ids(tokens)\n",
        "        ids = ids[:max_length] + [0] * (max_length - len(ids))\n",
        "\n",
        "        mask = [1] * len(tokens)\n",
        "        mask = mask[:max_length] + [0] * (max_length - len(mask))\n",
        "\n",
        "        input_ids.append(ids)\n",
        "        attention_masks.append(mask)\n",
        "\n",
        "    return torch.tensor(input_ids), torch.tensor(attention_masks)\n",
        "\n",
        "# Prepare for batch processing\n",
        "batch_size = 32\n",
        "max_length = 128\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "model.to(device)\n",
        "\n",
        "predicted_labels = []\n",
        "\n",
        "# Create a progress bar\n",
        "with tqdm(total=len(prediction_df), desc=\"Predicting\", unit=\"sentence\") as pbar:\n",
        "    for i in range(0, len(prediction_df), batch_size):\n",
        "        batch_sentences = prediction_df['sentence'][i:i + batch_size]\n",
        "        input_ids, attention_masks = prepare_data(batch_sentences, tokenizer, max_length)\n",
        "\n",
        "        input_ids = input_ids.to(device)\n",
        "        attention_masks = attention_masks.to(device)\n",
        "\n",
        "        with torch.no_grad():\n",
        "            outputs = model(input_ids=input_ids, attention_mask=attention_masks)\n",
        "            logits = outputs.logits\n",
        "\n",
        "        # Convert logits to probabilities\n",
        "        probs = torch.softmax(logits, dim=-1)\n",
        "\n",
        "        # Convert probabilities to predicted labels\n",
        "        batch_predicted_labels = torch.argmax(probs, dim=-1).cpu().numpy()\n",
        "\n",
        "        predicted_labels.extend(batch_predicted_labels.tolist())\n",
        "\n",
        "        # Update progress bar\n",
        "        pbar.update(len(batch_sentences))\n",
        "\n",
        "# Add the predicted label classes to the DataFrame\n",
        "prediction_df['resp_s_token_pred'] = predicted_labels\n",
        "\n",
        "# Save the updated DataFrame\n",
        "updated_prediction_data_path = '/content/gdrive/MyDrive/TimePressure/migration_sample_with_predictions_g.feather'\n",
        "prediction_df.to_feather(updated_prediction_data_path)\n",
        "\n",
        "# Display the updated DataFrame\n",
        "print(prediction_df.head())\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b6889e9d-7302-4fa9-93df-8c4d85ed8375",
        "id": "E9nFn3XS7FGe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Predicting: 100%|██████████| 74889/74889 [02:32<00:00, 490.91sentence/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   speech_id  sentence_id       date  \\\n",
            "0          0            1 2017-05-05   \n",
            "1          0            2 2017-05-05   \n",
            "2          0            3 2017-05-05   \n",
            "3          0            4 2017-05-05   \n",
            "4          0            5 2017-05-05   \n",
            "\n",
            "                                               title              speaker  \\\n",
            "0  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "1  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "2  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "3  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "4  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "\n",
            "   portfolio context                                             pdfurl  \\\n",
            "0  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "1  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "2  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "3  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "4  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "\n",
            "                                                 url text.annotated  ...  \\\n",
            "0  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "1  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "2  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "3  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "4  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "\n",
            "                                       sentence_text  \\\n",
            "0  Caro Antonio, Cari amici, Monsieur le Ministre...   \n",
            "1  I am hesitating between English and French, bu...   \n",
            "2  And also because the French will have election...   \n",
            "3  Mais avant de faire ça, je dois vous dire que ...   \n",
            "4  et donc je ne peux pas faire un discours magis...   \n",
            "\n",
            "                                    title_translated pdfonly     probs  \\\n",
            "0  ''With an outside view'' – Speech by President...   FALSE  0.785897   \n",
            "1  ''With an outside view'' – Speech by President...   FALSE  0.970456   \n",
            "2  ''With an outside view'' – Speech by President...   FALSE  0.991257   \n",
            "3  ''With an outside view'' – Speech by President...   FALSE  0.995607   \n",
            "4  ''With an outside view'' – Speech by President...   FALSE   0.99236   \n",
            "\n",
            "  langdetect_sentence                                sentence_translated  \\\n",
            "0                  en  Caro Antonio, Cari amici, Mr. Minister, Mr. Ma...   \n",
            "1                  en  I am hesitating between English and French, bu...   \n",
            "2                  en  And also because the French will have election...   \n",
            "3                  fr  But before I do this, I have to tell you that ...   \n",
            "4                  fr  And so I can't make a masterful speech, but I'...   \n",
            "\n",
            "                                    sentence_text_en  year  \\\n",
            "0  Caro Antonio, Cari amici, Mr. Minister, Mr. Ma...  2017   \n",
            "1  I am hesitating between English and French, bu...  2017   \n",
            "2  And also because the French will have election...  2017   \n",
            "3  But before I do this, I have to tell you that ...  2017   \n",
            "4  And so I can't make a masterful speech, but I'...  2017   \n",
            "\n",
            "                                            sentence  \\\n",
            "0  Caro Antonio, Cari amici, Mr. Minister, Mr. Ma...   \n",
            "1  I am hesitating between English and French, bu...   \n",
            "2  And also because the French will have election...   \n",
            "3  But before I do this, I have to tell you that ...   \n",
            "4  And so I can't make a masterful speech, but I'...   \n",
            "\n",
            "                                   resp_s_token_pred  \n",
            "0  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
            "1  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
            "2  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
            "3  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
            "4  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
            "\n",
            "[5 rows x 23 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 11.2.2 Aggregation to sentence level predictions"
      ],
      "metadata": {
        "id": "1StbENHtg5OL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the updated DataFrame from Feather file\n",
        "updated_prediction_data_path = '/content/gdrive/MyDrive/TimePressure/migration_sample_with_predictions_g.feather'\n",
        "prediction_df = pd.read_feather(updated_prediction_data_path)"
      ],
      "metadata": {
        "id": "_sqhrB1s7TcY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Assuming prediction_df already has the token-level predictions ('tp_l_token_pred')\n",
        "\n",
        "# Initialize columns for the three aggregation methods\n",
        "prediction_df['resp_s_sentence_pred_method1'] = 0  # Method 1: Three-token rule\n",
        "prediction_df['resp_s_sentence_pred_majority'] = 0  # Method 2: Majority vote\n",
        "prediction_df['resp_s_sentence_pred_first_non_negative'] = 0  # Method 3: First non-negative\n",
        "\n",
        "# Method 1: Three-token rule\n",
        "sentence_level_predictions_method1 = {}\n",
        "for sentence in prediction_df['sentence'].unique():\n",
        "    # Get token-level predictions for the current sentence\n",
        "    token_predictions = prediction_df[prediction_df['sentence'] == sentence]['resp_s_token_pred'].tolist()\n",
        "    # Flatten the token predictions if needed\n",
        "    token_predictions = [item for sublist in token_predictions for item in sublist]\n",
        "\n",
        "    # Apply the three-token rule\n",
        "    num_positive_tokens = sum(pred == 1 for pred in token_predictions)  # Counting positive tokens\n",
        "    sentence_level_predictions_method1[sentence] = 1 if num_positive_tokens >= 3 else 0\n",
        "\n",
        "# Map method 1 predictions back to the DataFrame\n",
        "prediction_df['resp_s_sentence_pred_method1'] = prediction_df['sentence'].map(sentence_level_predictions_method1)\n",
        "\n",
        "# Method 2: Majority vote\n",
        "sentence_predictions_majority = {}\n",
        "for sentence in prediction_df['sentence'].unique():\n",
        "    token_predictions = prediction_df[prediction_df['sentence'] == sentence]['resp_s_token_pred'].tolist()\n",
        "    token_predictions = [item for sublist in token_predictions for item in sublist]\n",
        "\n",
        "    # Apply majority voting\n",
        "    if token_predictions:\n",
        "        sentence_predictions_majority[sentence] = max(set(token_predictions), key=token_predictions.count)\n",
        "    else:\n",
        "        sentence_predictions_majority[sentence] = -100\n",
        "\n",
        "# Map method 2 predictions back to the DataFrame\n",
        "prediction_df['resp_s_sentence_pred_majority'] = prediction_df['sentence'].map(sentence_predictions_majority)\n",
        "\n",
        "# Method 3: First non-negative label\n",
        "sentence_predictions_first_non_negative = {}\n",
        "for sentence in prediction_df['sentence'].unique():\n",
        "    token_predictions = prediction_df[prediction_df['sentence'] == sentence]['resp_s_token_pred'].tolist()\n",
        "    token_predictions = [item for sublist in token_predictions for item in sublist]\n",
        "\n",
        "    # Apply first non-negative label rule\n",
        "    sentence_predictions_first_non_negative[sentence] = next((pred for pred in token_predictions if pred != -100), -100)\n",
        "\n",
        "# Map method 3 predictions back to the DataFrame\n",
        "prediction_df['resp_s_sentence_pred_first_non_negative'] = prediction_df['sentence'].map(sentence_predictions_first_non_negative)\n",
        "\n",
        "# Calculate prevalence of positive labels for each method\n",
        "method1_positive_count = (prediction_df['resp_s_sentence_pred_method1'] == 1).sum()\n",
        "method2_positive_count = (prediction_df['resp_s_sentence_pred_majority'] == 1).sum()\n",
        "method3_positive_count = (prediction_df['resp_s_sentence_pred_first_non_negative'] == 1).sum()\n",
        "total_sentences = prediction_df['sentence'].nunique()\n",
        "\n",
        "method1_prevalence = method1_positive_count / total_sentences * 100\n",
        "method2_prevalence = method2_positive_count / total_sentences * 100\n",
        "method3_prevalence = method3_positive_count / total_sentences * 100\n",
        "\n",
        "# Display prevalence results\n",
        "print(f\"Method 1 (Three-token rule) Prevalence: {method1_prevalence:.2f}%\")\n",
        "print(f\"Method 2 (Majority vote) Prevalence: {method2_prevalence:.2f}%\")\n",
        "print(f\"Method 3 (First non-negative rule) Prevalence: {method3_prevalence:.2f}%\")\n",
        "\n",
        "# Calculate correlations between aggregation methods\n",
        "correlation_matrix = prediction_df[['resp_s_sentence_pred_method1', 'resp_s_sentence_pred_majority', 'resp_s_sentence_pred_first_non_negative']].corr()\n",
        "\n",
        "# Display correlation results\n",
        "print(\"\\nCorrelation between aggregation methods:\")\n",
        "print(correlation_matrix)\n",
        "\n",
        "# Display the updated DataFrame for verification\n",
        "print(prediction_df[['sentence', 'resp_s_token_pred', 'resp_s_sentence_pred_method1', 'resp_s_sentence_pred_majority', 'resp_s_sentence_pred_first_non_negative']].head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lb6xkSpmFFv5",
        "outputId": "2047570c-4580-4f78-b868-4843b9223aca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Method 1 (Three-token rule) Prevalence: 25.31%\n",
            "Method 2 (Majority vote) Prevalence: 4.64%\n",
            "Method 3 (First non-negative rule) Prevalence: 2.13%\n",
            "\n",
            "Correlation between aggregation methods:\n",
            "                                         resp_s_sentence_pred_method1  \\\n",
            "resp_s_sentence_pred_method1                                 1.000000   \n",
            "resp_s_sentence_pred_majority                                0.381714   \n",
            "resp_s_sentence_pred_first_non_negative                      0.254940   \n",
            "\n",
            "                                         resp_s_sentence_pred_majority  \\\n",
            "resp_s_sentence_pred_method1                                  0.381714   \n",
            "resp_s_sentence_pred_majority                                 1.000000   \n",
            "resp_s_sentence_pred_first_non_negative                       0.582304   \n",
            "\n",
            "                                         resp_s_sentence_pred_first_non_negative  \n",
            "resp_s_sentence_pred_method1                                            0.254940  \n",
            "resp_s_sentence_pred_majority                                           0.582304  \n",
            "resp_s_sentence_pred_first_non_negative                                 1.000000  \n",
            "                                            sentence  \\\n",
            "0  Caro Antonio, Cari amici, Mr. Minister, Mr. Ma...   \n",
            "1  I am hesitating between English and French, bu...   \n",
            "2  And also because the French will have election...   \n",
            "3  But before I do this, I have to tell you that ...   \n",
            "4  And so I can't make a masterful speech, but I'...   \n",
            "\n",
            "                                   resp_s_token_pred  \\\n",
            "0  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "1  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "2  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "3  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "4  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "\n",
            "   resp_s_sentence_pred_method1  resp_s_sentence_pred_majority  \\\n",
            "0                             0                              0   \n",
            "1                             0                              0   \n",
            "2                             0                              0   \n",
            "3                             0                              0   \n",
            "4                             0                              0   \n",
            "\n",
            "   resp_s_sentence_pred_first_non_negative  \n",
            "0                                        0  \n",
            "1                                        0  \n",
            "2                                        0  \n",
            "3                                        0  \n",
            "4                                        0  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the updated DataFrame\n",
        "updated_prediction_data_path = '/content/gdrive/MyDrive/TimePressure/migration_sample_with_sentence_predictions_g.feather'\n",
        "prediction_df.to_feather(updated_prediction_data_path)\n",
        "\n",
        "# Display the updated DataFrame\n",
        "print(prediction_df.head())"
      ],
      "metadata": {
        "id": "I8dUg24uFGYh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b10fc74-215b-4347-813c-28408225260d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   speech_id  sentence_id       date  \\\n",
            "0          0            1 2017-05-05   \n",
            "1          0            2 2017-05-05   \n",
            "2          0            3 2017-05-05   \n",
            "3          0            4 2017-05-05   \n",
            "4          0            5 2017-05-05   \n",
            "\n",
            "                                               title              speaker  \\\n",
            "0  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "1  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "2  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "3  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "4  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "\n",
            "   portfolio context                                             pdfurl  \\\n",
            "0  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "1  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "2  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "3  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "4  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "\n",
            "                                                 url text.annotated  ...  \\\n",
            "0  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "1  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "2  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "3  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "4  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "\n",
            "      probs  langdetect_sentence  \\\n",
            "0  0.785897                   en   \n",
            "1  0.970456                   en   \n",
            "2  0.991257                   en   \n",
            "3  0.995607                   fr   \n",
            "4   0.99236                   fr   \n",
            "\n",
            "                                 sentence_translated  \\\n",
            "0  Caro Antonio, Cari amici, Mr. Minister, Mr. Ma...   \n",
            "1  I am hesitating between English and French, bu...   \n",
            "2  And also because the French will have election...   \n",
            "3  But before I do this, I have to tell you that ...   \n",
            "4  And so I can't make a masterful speech, but I'...   \n",
            "\n",
            "                                    sentence_text_en  year  \\\n",
            "0  Caro Antonio, Cari amici, Mr. Minister, Mr. Ma...  2017   \n",
            "1  I am hesitating between English and French, bu...  2017   \n",
            "2  And also because the French will have election...  2017   \n",
            "3  But before I do this, I have to tell you that ...  2017   \n",
            "4  And so I can't make a masterful speech, but I'...  2017   \n",
            "\n",
            "                                            sentence  \\\n",
            "0  Caro Antonio, Cari amici, Mr. Minister, Mr. Ma...   \n",
            "1  I am hesitating between English and French, bu...   \n",
            "2  And also because the French will have election...   \n",
            "3  But before I do this, I have to tell you that ...   \n",
            "4  And so I can't make a masterful speech, but I'...   \n",
            "\n",
            "                                   resp_s_token_pred  \\\n",
            "0  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "1  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "2  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "3  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "4  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "\n",
            "  resp_s_sentence_pred_method1 resp_s_sentence_pred_majority  \\\n",
            "0                            0                             0   \n",
            "1                            0                             0   \n",
            "2                            0                             0   \n",
            "3                            0                             0   \n",
            "4                            0                             0   \n",
            "\n",
            "  resp_s_sentence_pred_first_non_negative  \n",
            "0                                       0  \n",
            "1                                       0  \n",
            "2                                       0  \n",
            "3                                       0  \n",
            "4                                       0  \n",
            "\n",
            "[5 rows x 26 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Draw random sample for posr-stratification."
      ],
      "metadata": {
        "id": "CDK18ABXu4wF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Aggregate token-level predictions to get sentence-level predictions\n",
        "sentence_level_predictions = {}\n",
        "\n",
        "# Iterate over the DataFrame rows\n",
        "for index, row in prediction_df.iterrows():\n",
        "    sentence = row['sentence']\n",
        "    token_predictions = row['resp_s_token_pred']\n",
        "\n",
        "    # Count the number of positive token predictions\n",
        "    num_positive_tokens = sum(token_predictions)\n",
        "\n",
        "    # If at least three token predictions are positive, consider the entire sentence as positive\n",
        "    if num_positive_tokens >= 3:\n",
        "        sentence_level_predictions[sentence] = 1\n",
        "    else:\n",
        "        sentence_level_predictions[sentence] = 0\n",
        "\n",
        "# Add sentence-level predictions to the DataFrame\n",
        "prediction_df['resp_s_sentence_pred'] = prediction_df['sentence'].map(sentence_level_predictions)\n",
        "\n",
        "# Separate positive and negative predictions\n",
        "positive_sentences_resp_s = prediction_df[prediction_df['resp_s_sentence_pred'] == 1]\n",
        "negative_sentences_resp_s = prediction_df[prediction_df['resp_s_sentence_pred'] == 0]\n",
        "\n",
        "# Randomly sample 1000 positive and 1000 negative sentences\n",
        "sampled_positive_resp_s = positive_sentences_resp_s.sample(n=1000, random_state=42, replace=True)\n",
        "sampled_negative_resp_s = negative_sentences_resp_s.sample(n=1000, random_state=42, replace=True)\n",
        "\n",
        "# Combine the sampled data into a balanced dataset\n",
        "balanced_sample_resp_s_df = pd.concat([sampled_positive_resp_s, sampled_negative_resp_s])\n",
        "\n",
        "# Save the balanced dataset as an Excel file\n",
        "excel_output_path = '/content/gdrive/MyDrive/TimePressure/balanced_resp_s_sentence_sample.xlsx'\n",
        "\n",
        "# Convert to Excel and save\n",
        "balanced_sample_resp_s_df.to_excel(excel_output_path, index=False)\n",
        "\n",
        "# Confirm the save location\n",
        "print(f\"The balanced dataset has been saved to: {excel_output_path}\")\n"
      ],
      "metadata": {
        "id": "PG_O6zsm7gQs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16ea7eaa-1f81-49d6-b3df-46127f5e9b3b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The balanced dataset has been saved to: /content/gdrive/MyDrive/TimePressure/balanced_resp_s_sentence_sample.xlsx\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the total number of sentences\n",
        "total_sentences = len(prediction_df)\n",
        "\n",
        "# Calculate the number of sentences with positive labels\n",
        "positive_sentences = prediction_df['resp_s_sentence_pred'].sum()\n",
        "\n",
        "# Calculate the percentage of positive labels\n",
        "percentage_positive = (positive_sentences / total_sentences) * 100\n",
        "\n",
        "print(\"Percentage of positive labels:\", percentage_positive)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d1945a1-55e3-4e82-e7cf-e05eb906626e",
        "id": "Ychtn1737ghc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of positive labels: 5.847320701304597\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 11.2.3 Visualize predictions"
      ],
      "metadata": {
        "id": "QEPr4oxsgsJP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "# Load the saved DataFrame\n",
        "output_path = '/content/gdrive/MyDrive/TimePressure/migration_sample_with_sentence_predictions_g.feather'\n",
        "prediction_df = pd.read_feather(output_path)\n",
        "\n",
        "# Display all column names\n",
        "print(\"Columns in the saved DataFrame:\")\n",
        "print(prediction_df.columns.tolist())\n",
        "\n",
        "# Optionally, display the first few rows to see sample data\n",
        "print(\"\\nSample data from the saved DataFrame:\")\n",
        "print(prediction_df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lxDVpabtrFNe",
        "outputId": "ba92c80b-4d2c-4894-b80b-20cbba51e325"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Columns in the saved DataFrame:\n",
            "['speech_id', 'sentence_id', 'date', 'title', 'speaker', 'portfolio', 'context', 'pdfurl', 'url', 'text.annotated', 'after.sampling', 'length', 'langdetect', 'sentence_text', 'title_translated', 'pdfonly', 'probs', 'langdetect_sentence', 'sentence_translated', 'sentence_text_en', 'year', 'sentence', 'resp_s_token_pred', 'resp_s_sentence_pred_method1', 'resp_s_sentence_pred_majority', 'resp_s_sentence_pred_first_non_negative']\n",
            "\n",
            "Sample data from the saved DataFrame:\n",
            "   speech_id  sentence_id       date  \\\n",
            "0          0            1 2017-05-05   \n",
            "1          0            2 2017-05-05   \n",
            "2          0            3 2017-05-05   \n",
            "3          0            4 2017-05-05   \n",
            "4          0            5 2017-05-05   \n",
            "\n",
            "                                               title              speaker  \\\n",
            "0  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "1  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "2  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "3  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "4  ''Avec une vue sur l'extérieur'' – Discours du...  jean claude juncker   \n",
            "\n",
            "   portfolio context                                             pdfurl  \\\n",
            "0  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "1  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "2  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "3  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "4  President      NA  https://ec.europa.eu/commission/presscorner/ap...   \n",
            "\n",
            "                                                 url text.annotated  ...  \\\n",
            "0  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "1  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "2  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "3  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "4  https://ec.europa.eu/commission/presscorner/de...           None  ...   \n",
            "\n",
            "      probs  langdetect_sentence  \\\n",
            "0  0.785897                   en   \n",
            "1  0.970456                   en   \n",
            "2  0.991257                   en   \n",
            "3  0.995607                   fr   \n",
            "4   0.99236                   fr   \n",
            "\n",
            "                                 sentence_translated  \\\n",
            "0  Caro Antonio, Cari amici, Mr. Minister, Mr. Ma...   \n",
            "1  I am hesitating between English and French, bu...   \n",
            "2  And also because the French will have election...   \n",
            "3  But before I do this, I have to tell you that ...   \n",
            "4  And so I can't make a masterful speech, but I'...   \n",
            "\n",
            "                                    sentence_text_en  year  \\\n",
            "0  Caro Antonio, Cari amici, Mr. Minister, Mr. Ma...  2017   \n",
            "1  I am hesitating between English and French, bu...  2017   \n",
            "2  And also because the French will have election...  2017   \n",
            "3  But before I do this, I have to tell you that ...  2017   \n",
            "4  And so I can't make a masterful speech, but I'...  2017   \n",
            "\n",
            "                                            sentence  \\\n",
            "0  Caro Antonio, Cari amici, Mr. Minister, Mr. Ma...   \n",
            "1  I am hesitating between English and French, bu...   \n",
            "2  And also because the French will have election...   \n",
            "3  But before I do this, I have to tell you that ...   \n",
            "4  And so I can't make a masterful speech, but I'...   \n",
            "\n",
            "                                   resp_s_token_pred  \\\n",
            "0  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "1  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "2  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "3  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "4  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
            "\n",
            "  resp_s_sentence_pred_method1 resp_s_sentence_pred_majority  \\\n",
            "0                            0                             0   \n",
            "1                            0                             0   \n",
            "2                            0                             0   \n",
            "3                            0                             0   \n",
            "4                            0                             0   \n",
            "\n",
            "  resp_s_sentence_pred_first_non_negative  \n",
            "0                                       0  \n",
            "1                                       0  \n",
            "2                                       0  \n",
            "3                                       0  \n",
            "4                                       0  \n",
            "\n",
            "[5 rows x 26 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "# Assuming prediction_df is already loaded and contains 'date', 'tp_h_sentence_pred', and 'portfolio' columns\n",
        "\n",
        "# Ensure the date column is in datetime format\n",
        "prediction_df['date'] = pd.to_datetime(prediction_df['date'])\n",
        "\n",
        "# Extract the year from the date column\n",
        "prediction_df['year'] = prediction_df['date'].dt.year\n",
        "\n",
        "# Calculate positive sentences per year for 'President' portfolio\n",
        "president_df = prediction_df[prediction_df['portfolio'] == 'President']\n",
        "positive_sentences_president = president_df.groupby('year')['resp_s_sentence_pred_first_non_negative'].sum()\n",
        "unique_speech_ids_president = president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Calculate positive sentences per year for non-'President' portfolios\n",
        "non_president_df = prediction_df[prediction_df['portfolio'] != 'President']\n",
        "positive_sentences_non_president = non_president_df.groupby('year')['resp_s_sentence_pred_first_non_negative'].sum()\n",
        "unique_speech_ids_non_president = non_president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Calculate combined positive sentences per year\n",
        "combined_sentences_per_year = positive_sentences_president + positive_sentences_non_president\n",
        "\n",
        "# Plot the results\n",
        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "# Plot line for 'President' portfolio\n",
        "ax1.plot(positive_sentences_president.index, positive_sentences_president.values, marker='o', color='grey', linestyle='-', label='President')\n",
        "ax1.set_ylabel('Number of Explicit Ease of Time Pressure Invocations', color='black')\n",
        "ax1.tick_params(axis='y', labelcolor='blue')\n",
        "\n",
        "# Plot line for non-'President' portfolios\n",
        "ax1.plot(positive_sentences_non_president.index, positive_sentences_non_president.values, marker='o', color='grey', linestyle='--', label='Commissioner')\n",
        "ax1.tick_params(axis='y', labelcolor='black')\n",
        "\n",
        "# Plot combined line\n",
        "ax1.plot(combined_sentences_per_year.index, combined_sentences_per_year.values, marker='o', color='black', linestyle='-', label='Combined')\n",
        "ax1.set_ylabel('Number of Explicit Low Time Pressure Invocations', color='black')\n",
        "\n",
        "# Set y-axis limits for ax1\n",
        "ax1.set_ylim(0, 400)\n",
        "\n",
        "# Bar plot for unique speech_id for both 'President' and non-'President' portfolios\n",
        "ax2 = ax1.twinx()\n",
        "ax2.bar(unique_speech_ids_president.index, unique_speech_ids_president.values, width=0.4, align='center', color='lightblue', alpha=0.5, label='Speeches President')\n",
        "ax2.bar(unique_speech_ids_non_president.index + 0.4, unique_speech_ids_non_president.values, width=0.4, align='center', color='lightgrey', alpha=0.5, label='Speeches Commissioner')\n",
        "ax2.set_ylabel('Number of speeches', color='gray')\n",
        "ax2.tick_params(axis='y', labelcolor='gray')\n",
        "\n",
        "# Add vertical lines with Commissioner tenure labels and move them further below\n",
        "events = {\n",
        "    '1995': 'Gradin',\n",
        "    '1999': 'Vitorino',\n",
        "    '2004': 'Fratini',\n",
        "    '2008': 'Barrot',\n",
        "    '2010': 'Malmström',\n",
        "    '2014': 'Avramopoulos',\n",
        "    '2019': 'Johansson'\n",
        "}\n",
        "\n",
        "# Plot vertical lines for each event and move the labels lower to avoid overlap with the legend\n",
        "for year, name in events.items():\n",
        "    ax1.axvline(x=int(year), color='darkgrey', linestyle='--', lw=1)\n",
        "    ax1.text(int(year), ax1.get_ylim()[0] + 200, name, color='darkgrey', rotation=90, verticalalignment='bottom', horizontalalignment='center')\n",
        "\n",
        "# Set x-axis to show every year and rotate labels vertically\n",
        "ax1.set_xticks(range(prediction_df['year'].min(), prediction_df['year'].max() + 1))\n",
        "ax1.set_xticklabels(range(prediction_df['year'].min(), prediction_df['year'].max() + 1), rotation=90)\n",
        "\n",
        "# Add legend\n",
        "lines, labels = ax1.get_legend_handles_labels()\n",
        "lines2, labels2 = ax2.get_legend_handles_labels()\n",
        "ax1.legend(lines + lines2, labels + labels2, loc='upper left')\n",
        "\n",
        "# Adjust layout\n",
        "fig.tight_layout()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "bPbpqoTi82uq",
        "outputId": "3fac545e-6552-489c-e83e-4f436513ff88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "# Load the asylum data\n",
        "asylum_path = '/content/gdrive/MyDrive/TimePressure/asylum_year.xlsx'\n",
        "asylum_df = pd.read_excel(asylum_path)\n",
        "\n",
        "# Rename and scale the asylum data for visibility\n",
        "asylum_df.rename(columns={'asylum_nr_year': 'asylum_total'}, inplace=True)\n",
        "asylum_df['asylum_total_scaled'] = asylum_df['asylum_total'] / 10000\n",
        "\n",
        "# Assuming prediction_df is already loaded and contains 'date', 'resp_s_sentence_pred_first_non_negative', and 'portfolio' columns\n",
        "prediction_df['date'] = pd.to_datetime(prediction_df['date'])\n",
        "prediction_df['year'] = prediction_df['date'].dt.year\n",
        "\n",
        "# Calculate positive sentences per year for 'President' portfolio\n",
        "president_df = prediction_df[prediction_df['portfolio'] == 'President']\n",
        "positive_sentences_president = president_df.groupby('year')['resp_s_sentence_pred_first_non_negative'].sum()\n",
        "unique_speech_ids_president = president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Calculate positive sentences per year for non-'President' portfolios\n",
        "non_president_df = prediction_df[prediction_df['portfolio'] != 'President']\n",
        "positive_sentences_non_president = non_president_df.groupby('year')['resp_s_sentence_pred_first_non_negative'].sum()\n",
        "unique_speech_ids_non_president = non_president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Calculate combined positive sentences per year\n",
        "combined_sentences_per_year = positive_sentences_president + positive_sentences_non_president\n",
        "\n",
        "# Plot the results\n",
        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "\n",
        "# Plot combined line\n",
        "ax1.plot(combined_sentences_per_year.index, combined_sentences_per_year.values, marker='o', color='black', linestyle='-', label='Explicit Low Time Pressure Invocations')\n",
        "ax1.set_ylabel('Number of Explicit Low Time Pressure Invocations', color='black')\n",
        "\n",
        "# Plot scaled asylum numbers on the same axis\n",
        "ax1.plot(asylum_df['year'], asylum_df['asylum_total_scaled'], color='darkgrey', marker='s', linestyle='-', label='Yearly European Asylum Numbers (10,000)')\n",
        "ax1.set_ylim(0, 400)\n",
        "\n",
        "# Bar plot for unique speech_id for both 'President' and non-'President' portfolios\n",
        "ax2 = ax1.twinx()\n",
        "ax2.bar(unique_speech_ids_president.index, unique_speech_ids_president.values, width=0.4, align='center', color='lightblue', alpha=0.5, label='Speeches President')\n",
        "ax2.bar(unique_speech_ids_non_president.index + 0.4, unique_speech_ids_non_president.values, width=0.4, align='center', color='lightgrey', alpha=0.5, label='Speeches Commissioner')\n",
        "ax2.set_ylabel('Number of speeches', color='gray')\n",
        "ax2.tick_params(axis='y', labelcolor='gray')\n",
        "\n",
        "# Add vertical lines with Commissioner tenure labels and move them lower\n",
        "events = {\n",
        "    1995: 'Gradin',\n",
        "    1999: 'Vitorino',\n",
        "    2004: 'Fratini',\n",
        "    2008: 'Barrot',\n",
        "    2010: 'Malmström',\n",
        "    2014: 'Avramopoulos',\n",
        "    2019: 'Johansson'\n",
        "}\n",
        "\n",
        "for year, name in events.items():\n",
        "    ax1.axvline(x=year, color='darkgrey', linestyle='--', lw=1)\n",
        "    ax1.text(year, ax1.get_ylim()[0] + 200, name, color='darkgrey', rotation=90, verticalalignment='bottom', horizontalalignment='center')\n",
        "\n",
        "# Set x-axis to show every year and rotate labels vertically\n",
        "ax1.set_xticks(range(prediction_df['year'].min(), prediction_df['year'].max() + 1))\n",
        "ax1.set_xticklabels(range(prediction_df['year'].min(), prediction_df['year'].max() + 1), rotation=90)\n",
        "\n",
        "# Add legend\n",
        "lines, labels = ax1.get_legend_handles_labels()\n",
        "lines2, labels2 = ax2.get_legend_handles_labels()\n",
        "ax1.legend(lines + lines2, labels + labels2, loc='upper left')\n",
        "\n",
        "# Adjust layout\n",
        "fig.tight_layout()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "ybNYtBH1u2L8",
        "outputId": "d2256ec1-cc5f-4529-a1e5-7f14806a6189"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "# Assuming prediction_df is already loaded and contains 'date', 'resp_s_sentence_pred', and 'portfolio' columns\n",
        "\n",
        "# Ensure the date column is in datetime format\n",
        "prediction_df['date'] = pd.to_datetime(prediction_df['date'])\n",
        "\n",
        "# Extract the year from the date column\n",
        "prediction_df['year'] = prediction_df['date'].dt.year\n",
        "\n",
        "# Calculate positive sentences per year for 'President' portfolio\n",
        "president_df = prediction_df[prediction_df['portfolio'] == 'President']\n",
        "positive_sentences_president = president_df.groupby('year')['resp_s_sentence_pred'].sum()\n",
        "unique_speech_ids_president = president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Calculate positive sentences per year for non-'President' portfolios\n",
        "non_president_df = prediction_df[prediction_df['portfolio'] != 'President']\n",
        "positive_sentences_non_president = non_president_df.groupby('year')['resp_s_sentence_pred'].sum()\n",
        "unique_speech_ids_non_president = non_president_df.groupby('year')['speech_id'].nunique()\n",
        "\n",
        "# Calculate combined positive sentences per year\n",
        "combined_sentences_per_year = positive_sentences_president + positive_sentences_non_president\n",
        "\n",
        "# Plot the results\n",
        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "# Plot line for 'President' portfolio\n",
        "ax1.plot(positive_sentences_president.index, positive_sentences_president.values, marker='o', color='grey', linestyle='-', label='President')\n",
        "ax1.set_ylabel('Number of Explicit Ease of Time Pressure Invocations', color='black')\n",
        "ax1.tick_params(axis='y', labelcolor='blue')\n",
        "\n",
        "# Plot line for non-'President' portfolios\n",
        "ax1.plot(positive_sentences_non_president.index, positive_sentences_non_president.values, marker='o', color='grey', linestyle='--', label='Commissioner')\n",
        "ax1.tick_params(axis='y', labelcolor='black')\n",
        "\n",
        "# Plot combined line\n",
        "ax1.plot(combined_sentences_per_year.index, combined_sentences_per_year.values, marker='o', color='black', linestyle='-', label='Combined')\n",
        "\n",
        "# Set y-axis limits for ax1\n",
        "ax1.set_ylim(0, 550)\n",
        "\n",
        "# Bar plot for unique speech_id for both 'President' and non-'President' portfolios\n",
        "ax2 = ax1.twinx()\n",
        "ax2.bar(unique_speech_ids_president.index, unique_speech_ids_president.values, width=0.4, align='center', color='lightblue', alpha=0.5, label='Speeches President')\n",
        "ax2.bar(unique_speech_ids_non_president.index + 0.4, unique_speech_ids_non_president.values, width=0.4, align='center', color='lightgrey', alpha=0.5, label='Speeches Commissioner')\n",
        "ax2.set_ylabel('Number of speeches', color='gray')\n",
        "ax2.tick_params(axis='y', labelcolor='gray')\n",
        "\n",
        "# Add vertical lines with Commissioner tenure labels and move them further below\n",
        "events = {\n",
        "    '1995': 'Gradin',\n",
        "    '1999': 'Vitorino',\n",
        "    '2004': 'Fratini',\n",
        "    '2008': 'Barrot',\n",
        "    '2010': 'Malmström',\n",
        "    '2014': 'Avramopoulos',\n",
        "    '2019': 'Johansson'\n",
        "}\n",
        "\n",
        "# Plot vertical lines for each event and move the labels lower to avoid overlap with the legend\n",
        "for year, name in events.items():\n",
        "    ax1.axvline(x=int(year), color='darkgrey', linestyle='--', lw=1)\n",
        "    ax1.text(int(year), ax1.get_ylim()[0] + 150, name, color='darkgrey', rotation=90, verticalalignment='bottom', horizontalalignment='center')\n",
        "\n",
        "# Set x-axis to show every year and rotate labels vertically\n",
        "ax1.set_xticks(range(prediction_df['year'].min(), prediction_df['year'].max() + 1))\n",
        "ax1.set_xticklabels(range(prediction_df['year'].min(), prediction_df['year'].max() + 1), rotation=90)\n",
        "\n",
        "# Add legend\n",
        "lines, labels = ax1.get_legend_handles_labels()\n",
        "lines2, labels2 = ax2.get_legend_handles_labels()\n",
        "ax1.legend(lines + lines2, labels + labels2, loc='upper left')\n",
        "\n",
        "# Adjust layout\n",
        "fig.tight_layout()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "kTrPYfh2CViA",
        "outputId": "c3fbeacf-e573-47d2-c303-bc32ab8a411c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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ExcUhLCwMsbGxLX58fcnlcrRr1+6Bx7GxsYGLi0szRPRgampq7vkcm27jqaqqwvnz51FVVSV0KGQg1tB4xJhrMcYsRsyzaWAdiYTBptsI0tLS8Pnnn2Pjxo3Yvn07Nm7ciM8//xxpaWktetwZM2ZAIpHg+PHjiImJgb+/P7p37445c+bg6NGjAICsrCxERUVBLpfDzs4O48ePR35+vnaMuku+v/76a3h7e0Mul2PGjBlQqVRYvnw53Nzc4OLigiVLlugcWyKRYM2aNXj00Udha2uLgIAAHDlyBFeuXMHQoUMhk8kwYMAAZGRk1DtWncTERPTt2xcymQwODg4YOHAgrl+/DgBISUlBWFgYFAoF7Ozs0KdPH5w8eRJAw5eXr1q1Cp06dYKlpSW6du2Kb775pl68a9euxeOPPw5bW1t06dIFv/76q84+f/zxB0aPHg25XA5XV1c888wzuH37tvb5oUOH4uWXX8arr76K9u3bIyIiQs+KERERERGRqWHT3cLS0tKwdetWlJSU6GwvKSnB1q1bW6zxLiwsRFxcHGJjYxucLMPBwQFqtRpRUVEoLCxEUlIS4uPjcfXqVUyYMEFn34yMDOzZswdxcXH4/vvvsW7dOkRGRiInJwdJSUn48MMP8fbbb+PYsWM6r1u8eDEmT56Ms2fPolu3bnjqqafw0ksvYf78+Th58iQ0Gg1efvnlBuNXKpWIjo7GkCFDcO7cORw5cgQvvvgiJBIJAGDSpEnw9PTEiRMncOrUKbz55puwsLBocKwdO3Zg1qxZmDt3Lv744w+89NJLePbZZ3HgwAGd/RYtWoTx48fj3LlzGDNmDCZNmoTCwkIAQFFREYYNG4bg4GCcPHkScXFxyM/Px/jx43XG2LhxIywtLZGcnIzVq1ffp0JERERERNQWmAsdgFjd79JhqVQKc3NzqNVqxMXF3XecuLg4dO3aVTuh1r3G1XfCiytXrkCj0aBbt2733CchIQHnz59HZmYmvLy8AACbNm1C9+7dceLECTz88MMA7l4K/fXXX0OhUCAwMBBhYWFIT0/H7t27IZVK0bVrV3z44Yc4cOAA+vXrpx3/2Wef1Talb7zxBkJDQ/HOO+9ozwDPmjULzz77bIOxlZSUoLi4GI8++ig6deoEAAgICNA+n5WVhddff137/rp06XLP9/nxxx9j6tSpmDFjBgBoz/R//PHHCAsL0+43depUTJw4EQCwdOlSfPHFFzh+/DhGjRqFL7/8EsHBwVi6dKl2/6+//hpeXl64dOkS/P39tXEsX778nrEQEREREVHbwqbbQMuWLbvnc126dMFTTz2FrKyseme4/66kpARZWVnw9fUFAHz++eeoqKiot9+CBQv0ik+j0TS6T1paGry8vLQNNwAEBgbCwcEBaWlp2qbb19cXCoVCu4+rqyvMzMx0Zt52dXXFzZs3dcYPCgrSeR4AevbsqbOtqqoKJSUlsLOz03mtk5MTpk6dioiICISHh2PEiBEYP3483N3dAdxtnJ9//nl88803GDFiBJ588kltc97Q+3zxxRd1tg0cOBCff/75PeOVyWSws7PTvqeUlBQcOHAAcrm83vgZGRnaprtPnz4NxvB3EokElpaW2jP31HIsLCzg6el5zyshqPVjDY1HjLkWY8xixDybBtaRSBi8vLwFlZaWNut++ujSpQskEgkuXrz4wGP9/T/MEomkwW1/nxTsr/vUNZcNbbvXZGLr16/HkSNHMGDAAGzZsgX+/v7ae9EXLlyICxcuIDIyEv/73/8QGBiIHTt2GPgO68f79/dUVlaGsWPH4uzZszo/ly9fxuDBg7Wvaeq6l2ZmZpDL5TAzM3ugmKlxcrkcAwYMaPALExIH1tB4xJhrMcYsRsyzaWAdiYTBM90Gmj9//j2fqzsD/Nezw/fz1/1mzZr1YIH9HycnJ0RERGDlypWYOXNmvWawqKgIAQEByM7ORnZ2tvZsd2pqKoqKihAYGNgscTyo4OBgBAcHY/78+QgNDcXmzZvRv39/AIC/vz/8/f0xe/ZsTJw4EevXr8fjjz9eb4yAgAAkJydjypQp2m3Jycl6vcfevXvjp59+gq+vL8zNH/xjo9FooFarIZVKeba7halUKlRXV8PKyopfcogUa2g8Ysy1GGMWI+bZNLCORMLgmW4DWVpa3vOnrinz9vaud9n039nZ2cHb27vRcQ2xcuVKqFQq9O3bFz/99BMuX76MtLQ0fPHFFwgNDcWIESPQs2dPTJo0CadPn8bx48cxefJkDBkyBCEhIQYds7lkZmZi/vz5OHLkCK5fv459+/bh8uXLCAgIQGVlJV5++WUkJibi+vXrSE5OxokTJ3Tu+f6r119/HRs2bMCqVatw+fJlfPrpp9i+fTtee+21JscTGxuLwsJCTJw4ESdOnEBGRgb27t2LZ599FiqVSu/3p1KpUFxcbNBrST8lJSXYtWtXo7d6UOvFGhqPGHMtxpjFiHk2DawjkTAEbboXLlwIiUSi8/PXib+qqqoQGxuLdu3aQS6XIyYmRmc5K+DuhFqRkZGwtbWFi4sLXn/9dSiVSmO/lQZJpVKMGjXqvvuMGjVK597o5uTn54fTp08jLCwMc+fORY8ePRAeHo6EhASsWrUKEokEv/zyCxwdHTF48GCMGDECfn5+2LJlS4vEow9bW1tcvHhRu9TZiy++iNjYWLz00kswMzNDQUEBJk+eDH9/f4wfPx6jR4/GokWLGhwrOjoan3/+OT7++GN0794da9aswfr16zF06NAmx+Ph4YHk5GSoVCqMHDkSPXv2xKuvvgoHB4cWqx8REREREYmfRNOUGbdayMKFC7Ft2zbs379fu83c3Bzt27cHAEyfPh2//fYbNmzYAHt7e7z88suQSqVITk4GcPdsYa9eveDm5oaPPvoIubm5mDx5Ml544QWdWaYbk5OTAy8vL2RnZ8PT01PnuaqqKmRmZqJjx46wtrY26H2mpaUhLi5O51tFOzs7jBo16p5nZ8m0KZVK7QRyzXG5uilojs9aQ+7cuYP4+HiEh4fD0dGx2cYl42ENjUeMuRZjzGIkRJ7/fqKlIXUTtVLT8PNCrdH9ejFTIfhf++bm5nBzc6u3vbi4GOvWrcPmzZsxbNgwAHcn1goICMDRo0fRv39/7Nu3D6mpqdi/fz9cXV3Rq1cvLF68GG+88QYWLlxo8GXZzS0gIABdu3ZFVlYWSktLoVAo4O3tzTOkREREREREJk7wru/y5cvw8PCAn58fJk2ahKysLADAqVOnUFtbixEjRmj37datG7y9vXHkyBEAwJEjR9CzZ0+dbzkjIiJQUlKCCxcuGPeNNEIqlcLX1xc9e/aEr68vG24iIiIiIqI2QNAz3f369cOGDRvQtWtX5ObmYtGiRXjkkUfwxx9/IC8vD5aWlnBwcNB5jaurK/Ly8gAAeXl59S4rqntct09DqqurUV1drX3cEkt2Ed2PmZkZL+syEgcHB8TExPCLLhFjDY1HjLkWY8xixDybBtaRSBiCNt2jR4/W/h4UFIR+/frBx8cHW7duhY2NTYsdd9myZfecdIvIGLhMmPFIJBIuiyJyrKHxiDHXYoxZjJhn08A6EgmjVX3N5eDgAH9/f1y5cgVubm6oqalBUVGRzj75+fnae8Dd3NzqTbJR97ih+8TrzJ8/H8XFxdqf1NTU5n0jRI1QqVQoKSnhkmFGUFpaigMHDvCKFhFjDY1HjLkWY8xixDybBtaRSBitqukuKytDRkYG3N3d0adPH1hYWCAhIUH7fHp6OrKyshAaGgoACA0Nxfnz53Hz5k3tPvHx8bCzs0NgYOA9j2NlZQU7Ozvtj0KhaLk3RdQAjUYDpVIJARcPaDOUSiVu3brVapYSJP2xhsYjxlyLMWYxYp5NA+tIJAxBLy9/7bXXMHbsWPj4+ODPP//EggULYGZmhokTJ8Le3h7PPfcc5syZAycnJ9jZ2eGVV15BaGgo+vfvDwAYOXIkAgMD8cwzz2D58uXIy8vD22+/jdjYWFhZWQn51oiIiIiIiIiEbbpzcnIwceJEFBQUwNnZGYMGDcLRo0fh7OwMAFixYgWkUiliYmJQXV2NiIgIfPXVV9rXm5mZYdeuXZg+fTpCQ0Mhk8kwZcoUvPfee0K9JSIiIiIiIiItQZvuH3744b7PW1tbY+XKlVi5cuU99/Hx8cHu3bubOzQiIiIiIiKiB9aq7ukm0yGRSPDzzz/f8/lr165BIpHg7NmzgsciBKlUCplMxiU7jMDW1hYhISGwtbUVOhQyEGtoPGLMtRhjFiPm2TSwjkTC4F/8Ji4vLw+vvPIK/Pz8YGVlBS8vL4wdO1ZngjoheHl5ITc3Fz169BA0DqFIpVJYWVmx6TYCKysr7b9/EifW0HjEmGsxxixGzLNpYB2JhMG/+I1EpVIhMTER33//PRITE42yVNS1a9fQp08f/O9//8NHH32E8+fPIy4uDmFhYYiNjW3x49+PmZkZ3NzcYG4u6B0OglGr1aiuroZarRY6FJNXXV2Nq1evorq6WuhQyECsofGIMddijFmMmGfTwDoSCYNNtxFs374dvr6+CAsLw1NPPYWwsDD4+vpi+/btLXrcGTNmQCKR4Pjx44iJiYG/vz+6d++OOXPm4OjRowCArKwsREVFQS6Xw87ODuPHj9dZ+3zhwoXo1asXvv76a3h7e0Mul2PGjBlQqVRYvnw53Nzc4OLigiVLltQ7fm5uLkaPHg0bGxv4+flh27Zt2uf+fnl5YmIiJBIJEhIStJc9DRgwAOnp6Tpj/vLLL+jduzesra3h5+eHRYsW6Sx7cfnyZQwePBjW1tYIDAxEfHx8c6a02ajVapSXl7PpNoKKigqcPHkSFRUVQodCBmINjUeMuRZjzGLEPJsG1pFIGGy6W9j27dvxxBNPICcnR2f7jRs38MQTT7RY411YWIi4uDjExsZCJpPVe97BwQFqtRpRUVEoLCxEUlIS4uPjcfXqVUyYMEFn34yMDOzZswdxcXH4/vvvsW7dOkRGRiInJwdJSUn48MMP8fbbb+PYsWM6r3vnnXcQExODlJQUTJo0Cf/4xz+QlpZ237j/9a9/4ZNPPsHJkydhbm6OadOmaZ87dOgQJk+ejFmzZiE1NRVr1qzBhg0btA2/Wq3GuHHjYGlpiWPHjmH16tV44403DE0hERERERHRA2ub1/Y+AI1G0+RvB1UqFWbOnAmNRtPgOBKJBLNmzcKIESNgZmbW6Hi2traQSCRNOvaVK1eg0WjQrVu3e+6TkJCA8+fPIzMzE15eXgCATZs2oXv37jhx4gQefvhhAHeb2a+//hoKhQKBgYEICwtDeno6du/eDalUiq5du+LDDz/EgQMH0K9fP+34Tz75JJ5//nkAwOLFixEfH49///vfOsu+/d2SJUswZMgQAMCbb76JyMhIVFVVwdraGosWLcKbb76JKVOmAAD8/PywePFizJs3DwsWLMD+/ftx8eJF7N27Fx4eHgCApUuXYvTo0U3KGRERERERUXNj062niooKyOXyZhlLo9EgJycH9vb2Tdq/rKyswbPW9xq7MWlpafDy8tI23AAQGBgIBwcHpKWlaZtuX19fKBQK7T6urq4wMzPTmQTM1dUVN2/e1Bk/NDS03uPGZisPCgrS/u7u7g4AuHnzJry9vZGSkoLk5GSdS9lVKhWqqqpQUVGhfT91DXdDMRARERERERkTm24T1aVLF0gkEly8ePGBx7KwsNB5LJFIGtzWHPcn/3XcurP6deOWlZVh0aJFGDduXL3XWVtbP/CxjUkikcDc3LzJVy6Q4czNzeHs7NxmJ+0zBayh8Ygx12KMWYyYZ9PAOhIJg584Pdna2qKsrKxJ+x48eBBjxoxpdL/du3dj8ODBTTp2Uzk5OSEiIgIrV67EzJkz650hLyoqQkBAALKzs5Gdna09252amoqioiIEBgY2+Vj3cvToUUyePFnncXBwsMHj9e7dG+np6ejcuXODz9e9n9zcXO1Z8roJ41obMzMz2NnZCR1Gm6BQKBAWFiZ0GPQAWEPjEWOuxRizGDHPpoF1JBIGm249SSSSJl/iPXLkSHh6euLGjRsNXu4tkUjg6emJkSNHNumebn2tXLkSAwcORN++ffHee+8hKCgISqUS8fHxWLVqFVJTU9GzZ09MmjQJn332GZRKJWbMmIEhQ4YgJCTkgY//448/IiQkBIMGDcJ3332H48ePY926dQaP9+677+LRRx+Ft7c3nnjiCUilUqSkpOCPP/7A+++/jxEjRsDf3x9TpkzBRx99hJKSEvzrX/964PfREv7674Fnu1uWRqOBWq2GVCplrkWKNTQeMeZajDGLEfNsGlhHImFw9vIWZGZmhs8//xxA/caq7vFnn33WIg03cHeisdOnTyMsLAxz585Fjx49EB4ejoSEBKxatQoSiQS//PILHB0dMXjwYIwYMQJ+fn7YsmVLsxx/0aJF+OGHHxAUFIRNmzbh+++/f6Az6BEREdi1axf27duHhx9+GP3798eKFSvg4+MDAJBKpdixYwcqKyvRt29fPP/88w0uZdYaqFQq3Llzxyjrtbd1RUVF+Omnn1BUVCR0KGQg1tB4xJhrMcYsRsyzaWAdiYTBM90tbNy4cdi2bRtmzZqls2yYp6cnPvvsswbvT25O7u7u+PLLL/Hll182+Ly3tzd++eWXe75+4cKFWLhwoc62DRs21NsvMTFR53HdmdwZM2Y0OK6vr6/O2d6hQ4fWuxqgV69e9bZFREQgIiLinvH6+/vj0KFDDcZCRERERERkbGy6jWDcuHGIiorCoUOHtPcbP/LIIy12hpuIiIiIiIhaBzbdRmJmZoahQ4cKHQYREREREREZEe/pJiIiIiIiImohEg1veEVOTg68vLyQnZ0NT09PneeqqqqQmZmJjh07im4taGq9OHtofS31WVOpVKiuroaVlRVv6RAp1tB4xJhrMcYsRkLkOT8/v9F9XF1djRCJ6eDnhVqj+/VipoKXlxMJQCKR8P/sjMTMzEyvNe6p9WENjUeMuRZjzGLEPJsG1pFIGLy8nEgAKpUKZWVlXDLMCMrKyvD777+jrKxM6FDIQKyh8Ygx12KMWYyYZ9PAOhIJg003kQA0Gg1qamq4nJkR1NbWIicnB7W1tUKHQgZiDY1HjLkWY8xixDybBtaRSBhsuomIiIiIiIhaCJtuIiIiIiIiohbCppsEtXDhQvTq1UvoMFpcU97n1KlTER0dbZR4iIiIiIjIONh0m6hbt25h+vTp8Pb2hpWVFdzc3BAREYHk5GShQxPc1KlTIZFIIJFIYGlpic6dO+O9996DUqlssWO+9tprSEhI0D6WSqWwsbGBVNqyH8G28qXG/VhbW6Nnz55c8k/EWEPjEWOuxRizGDHPpoF1JBIGlwwzUOrtUqMeL7C9Qq/9Y2JiUFNTg40bN8LPzw/5+flISEhAQUFBC0UoLqNGjcL69etRXV2N3bt3IzY2FhYWFpg/f369fWtqamBpaflAx5PL5ZDL5drHdU03tTwbGxsEBAQIHQY9ANbQeMSYazHGLEbMs2lgHckUfPbZZyguLq63PSQkBJGRkVAqldi7dy8uXLgApVKJzp07Y8yYMTp/ixsbz3SboKKiIhw6dAgffvghwsLC4OPjg759+2L+/Pl47LHHtPtJJBKsWrUKo0ePho2NDfz8/LBt2zadsbKzszF+/Hg4ODjAyckJUVFRuHbtms4+a9euRUBAAKytrdGtWzd89dVXOs/n5ORg4sSJcHJygkwmQ0hICI4dO6azzzfffANfX1/Y29vjH//4B0pL//+XGmq1GsuWLUPHjh1hY2ODhx56SCfOO3fuYNKkSXB2doaNjQ26dOmC9evX3zdHdWf/fXx8MH36dIwYMQK//vorgP9/mfeSJUvg4eGBrl27NikXiYmJ6Nu3L2QyGRwcHDBw4EBcv34dQP0zzrW1tZg1axYcHBzQrl07zJs3r95M5o2978TEREgkEiQkJCAkJAS2trYYMGAA0tPTAQAbNmzAokWLkJKSoj2zv2HDhvvmxRTV1NTgxo0bqKmpEToUMhBraDxizLUYYxYj5tk0sI5kCl544QXMnTtX+/PMM88AALp37w4AiIuLw6VLl/Dkk09i6tSpKC0txdatW4UMWf+me+PGjfjtt9+0j+fNmwcHBwcMGDBA22CQsOrOqv7888+orq6+777vvPMOYmJikJKSgkmTJuEf//gH0tLSANxtDCMiIqBQKHDo0CEkJydDLpdj1KhR2v9Yf/fdd3j33XexZMkSpKWlYenSpXjnnXewceNGAHfXgxwyZAhu3LiBX3/9FSkpKZg3bx7UarU2hoyMDPz888/YtWsXdu3ahaSkJHzwwQfa55ctW4ZNmzZh9erVuHDhAmbPno2nn34aSUlJ2veQmpqKPXv2IC0tDatWrUL79u31ypmNjY3O/wElJCQgPT0d8fHx2LVrV6O5UCqViI6OxpAhQ3Du3DkcOXIEL774IiQSSYPH++STT7Bp0yb897//xeHDh1FYWIgdO3bo7NPY+67zr3/9C5988glOnjwJc3NzTJs2DQAwYcIEzJ07F927d0dubi5yc3MxYcIEvfJiCsrLy5GcnIzy8nKhQyEDsYbGI8ZcizFmMWKeTQPrSKZAJpNp+x25XI5Lly7B0dERPj4+qKqqwpkzZxAREYGOHTvCw8MDUVFRyM7ORk5OjmAx6315+dKlS7Fq1SoAwJEjR7By5UqsWLECu3btwuzZs7F9+/ZmD5L0Y25ujg0bNuCFF17A6tWr0bt3bwwZMgT/+Mc/EBQUpLPvk08+ieeffx4AsHjxYsTHx+Pf//43vvrqK2zZsgVqtRpr167VNo/r16+Hg4MDEhMTMXLkSCxYsACffPIJxo0bBwDo2LEjUlNTsWbNGkyZMgWbN2/GrVu3cOLECTg5OQEAOnfurBODWq3Ghg0boFDcvYT+mWeeQUJCApYsWYLq6mosXboU+/fvR2hoKADAz88Phw8fxpo1azBkyBBkZWUhODgYISEhAABfX98m50qj0SAhIQF79+7FK6+8ot0uk8mwdu1a7WXl33777X1zERISguLiYjz66KPo1KkTANz38q0vvvgCr776Kh5//HGYm5tj9erV2Lt3r/b5przvOkuWLNE+fvPNNxEZGYmqqirY2NhALpfD3Nwcbm5uTc4JEREREZEYqFQqnDt3DqGhoZBIJMjNzYVarYafn592n/bt28Pe3h7Z2dnw9PQUJE69m+7s7Gxt0/Tzzz8jJiYGL774IgYOHIihQ4c2d3xkoJiYGERGRuLQoUM4evQo9uzZg+XLl2Pt2rWYOnWqdr+6hu6vj8+ePQsASElJwZUrV7TNcJ2qqipkZGSgvLwcGRkZeO655/DCCy9on1cqlbC3twcAnD17FsHBwdqGuyG+vr46x3B3d8fNmzcBAFeuXEFFRQXCw8N1XlNTU4Pg4GAAwPTp0xETE4PTp09j5MiRiI6OxoABA+6bn127dkEul6O2thZqtRpPPfUUFi5cqH2+Z8+eOvdxN5aLkSNHYurUqYiIiEB4eDhGjBiB8ePHw93dvd6xi4uLkZubiz59+mi3mZubIyQkRHuJeVPed52/fpFSd7ybN2/C29v7vjkgIiIiImotSktLUVJSon1sZWUFKyur+77m4sWLqKqq0t7GWVZWBjMzs3qTBcpkMpSVlTV7zE2ld9Mtl8tRUFAAb29v7Nu3D3PmzAFwdzbEysrKZg+QDGdtbY3w8HCEh4fjnXfewfPPP48FCxboNN33U1ZWhj59+uC7776r95yzs7P2H+5///tf9OvXT+d5MzMzAGjSZGEWFhY6jyUSifby87pj/Pbbb+jQoYPOfnUfwtGjR+P69evYvXs34uPjMXz4cMTGxuLjjz++5zHDwsKwatUqWFpawsPDA+bmuh8FmUym87ixXAB3z3zPnDkTcXFx2LJlC95++23Ex8ejf//+jebg75ryvuv8NX91Z+H/evk+EREREVFrFxgYqPN4wYIFOifFGnLmzBl06dKl3omx1kbvpjs8PBzPP/88goODcenSJYwZMwYAcOHCBb0u6yXjCwwMxM8//6yz7ejRo5g8ebLO47ozqb1798aWLVvg4uICOzu7euPZ29vDw8MDV69exaRJkxo8ZlBQENauXYvCwsL7nu2+X8xWVlbIysrSuaT675ydnTFlyhRMmTIFjzzyCF5//fX7Nt0ymazeZe7301gu6gQHByM4OBjz589HaGgoNm/eXK/ptre3h7u7O86cOYNRo0YBuHt1wKlTp9C7d2+93ndjLC0toVKpDH69KZBKpbCzs2vx5dmo5bCGxiPGXIsxZjFink0D60itWWpqqs7JpsbOchcVFeHq1asYP368dptcLodKpUJVVZXO2e7y8nJBZy/Xu+leuXIl3n77bWRnZ+Onn35Cu3btAACnTp3CxIkTmz1A0l9BQQGefPJJTJs2DUFBQVAoFDh58iSWL1+OqKgonX1//PFHhISEYNCgQfjuu+9w/PhxrFu3DgAwadIkfPTRR4iKisJ7770HT09PXL9+Hdu3b8e8efPg6emJRYsWYebMmbC3t8eoUaNQXV2NkydP4s6dO5gzZw4mTpyIpUuXIjo6GsuWLdM2mx4eHvUubW+IQqHAa6+9htmzZ0OtVmPQoEEoLi5GcnIy7OzsMGXKFLz77rvo06cPunfvjurqauzatavZl8NoLBe1tbX4z3/+g8ceewweHh5IT0/H5cuXdb7Q+KtZs2Zh+fLl6NmzJ7p164ZPP/0URUVFer3vpvD19UVmZibOnj0LT09PKBSKRv8DZmrq/m2SeLGGxiPGXIsxZjFink0D60itmUKhuO/Jrb87e/YsZDIZ/P39tdvc3d0hlUpx9epV7Znz27dvo7i4GF5eXs0ec1Pp3XQ7ODjgyy+/rLd90aJFzRIQPTi5XI5+/fphxYoVyMjIQG1tLby8vPDCCy/grbfe0tl30aJF+OGHHzBjxgy4u7vj+++/1/4DtbW1xcGDB/HGG29g3LhxKC0tRYcOHTB8+HDtB+L555+Hra0tPvroI7z++uuQyWTo2bMnXn31VQB3z7Tu27cPc+fOxZgxY6BUKhEYGIiVK1c2+f0sXrwYzs7OWLZsGa5evQoHBwf07t1b+14sLS0xf/58XLt2DTY2NnjkkUfwww8/NEMm/7/GclFZWYmLFy9i48aNKCgogLu7O2JjY/HSSy81ON7cuXORm5uLKVOmQCqVYtq0aXj88cd11hxs7H03RUxMDLZv346wsDAUFRVh/fr1Tb69gIiIiIioNdJoNDh79iweeughnSs3rK2tERwcjH379sHGxgZWVlbYs2cPPD09BZtEDQAkmr8vDtwERUVFOH78OG7evKlz76hEItGukyYmOTk58PLyanBGu6qqKmRmZqJjx471bsgXO4lEgh07diA6OlroUNocpVKJ0tJSKBSKeveTt1Ut9Vm7c+cODhw4gLCwMDg6OjbbuGQ8rKHxiDHXYoxZjITIc35+fqP7uLq6GiES08HPC7VG9+vF7iUjIwPffvstXn75Ze2V13WUSiX27t2LP/74AyqVCp06dUJkZKS4Li/fuXMnJk2ahLKyMtjZ2emsQyzWpptICAZ830UGUiqVQodAD4g1NB4x5lqMMYsR82waWEcyBZ06dcKCBQsafM7c3ByRkZGIjIw0clT3pvcsCnPnzsW0adNQVlaGoqIi3LlzR/tTWFjYEjESERERERERiZLeZ7pv3LiBmTNnwtbWtiXiISPimVYiIiIiIqKWpfeZ7oiICJw8ebIlYiEiIiIiIiIyKXqf6Y6MjMTrr7+O1NRU9OzZExYWFjrPP/bYY80WHJGpMjMzg52dHczMzIQOxeQpFAqEh4dDoVAIHQoZiDU0HjHmWowxixHzbBpYRyJh6N10v/DCCwCA9957r95zEokEKpXqwaMiMnESiYSzlhuJubk5Z2gVOdbQeMSYazHGLEbMs2lgHYmEoffl5Wq1+p4/bLiJmkalUqG8vJyfGSMoLy/HqVOnUF5eLnQoZCDW0HjEmGsxxixGzLNpYB2JhKF3001ED06j0aC6upqT2RlBTU0NMjIyUFNTI3QoZCDW0HjEmGsxxixGzLNpYB2JhGFQ052UlISxY8eic+fO6Ny5Mx577DEcOnSouWMjIiIiIiIiEjW9m+5vv/0WI0aMgK2tLWbOnImZM2fCxsYGw4cPx+bNm1siRjJhCxcuRK9evYQOo9Vrrjxt2LABDg4ODzwOERERERE1jd5N95IlS7B8+XJs2bJF23Rv2bIFH3zwARYvXtwSMZIBbt26henTp8Pb2xtWVlZwc3NDREQEkpOThQ6tVcjLy8Mrr7wCPz8/WFlZwcvLC2PHjkVCQoLQoTXotddea5bYJkyYgEuXLjVDRERERERE1BR6T5989epVjB07tt72xx57DG+99VazBCUG+fn5Rj2eq6urXvvHxMSgpqYGGzduhJ+fH/Lz85GQkICCgoIWilA8rl27hoEDB8LBwQEfffQRevbsidraWuzduxexsbG4ePFii8cgkUhgbW0NiUTSpP3lcjnkcvkDH9fGxgY2NjYPPM6DqqmpgaWlpVGOZWVlBX9/f1hZWRnleNT8WEPjEWOuxRizGDHPpoF1JBKG3me6vby8Gjzjtn//fnh5eTVLUPRgioqKcOjQIXz44YcICwuDj48P+vbti/nz5+usoy6RSLBq1SqMHj0aNjY28PPzw7Zt23TGys7Oxvjx4+Hg4AAnJydERUXh2rVrOvusXbsWAQEBsLa2Rrdu3fDVV1/pPJ+Tk4OJEyfCyckJMpkMISEhOHbsmM4+33zzDXx9fWFvb49//OMfKC0t1T6nVquxbNkydOzYETY2NnjooYd04rxz5w4mTZoEZ2dn2NjYoEuXLli/fv098zNjxgxIJBIcP34cMTEx8Pf3R/fu3TFnzhwcPXpUu19WVhaioqIgl8thZ2eH8ePH63zZUnfJ99dffw1vb2/I5XLMmDEDKpUKy5cvh5ubG1xcXLBkyRKd40skEqxduxbjx4+HQqFAQEAAjhw5gitXrmDo0KGQyWQYMGAAMjIy6h2rTmJiIvr27QuZTAYHBwcMHDgQ169fBwCkpKQgLCwMCoUCdnZ26NOnD06ePAmg4cvLV61ahU6dOsHS0hJdu3bFN99802C8jz/+OGxtbdGlSxf8+uuvOvv88ccfGD16NORyOVxdXfHMM8/g9u3b2ueHDh2Kl19+Ga+++irat2+PiIiIe9anudna2qJXr16wtbU12jGpebGGxiPGXIsxZjFink0D60gkDL2b7rlz52LmzJmYPn06vvnmG3zzzTf45z//iVdffRWvvfZaS8RIeqo7K/rzzz+jurr6vvu+8847iImJQUpKCiZNmoR//OMfSEtLAwDU1tYiIiICCoUChw4dQnJyMuRyOUaNGqWd9fK7777Du+++iyVLliAtLQ1Lly7FO++8g40bNwIAysrKMGTIENy4cQO//vorUlJSMG/ePKjVam0MGRkZ+Pnnn7Fr1y7s2rULSUlJ+OCDD7TPL1u2DJs2bcLq1atx4cIFzJ49G08//TSSkpK07yE1NRV79uxBWloaVq1ahfbt2zf4fgsLCxEXF4fY2FjIZLJ6z9c1pGq1GlFRUSgsLERSUhLi4+Nx9epVTJgwQWf/jIwM7NmzB3Fxcfj++++xbt06REZGIicnB0lJSfjwww/x9ttv1/uSYfHixXjqqadw+vRpdOvWDU899RReeuklzJ8/HydPnoRGo8HLL7/c4HtQKpWIjo7GkCFDcO7cORw5cgQvvvii9qz5pEmT4OnpiRMnTuDUqVN48803YWFh0eBYO3bswKxZszB37lz88ccfeOmll/Dss8/iwIEDOvstWrQI48ePx7lz5zBmzBhMmjQJhYWFAO5+yTNs2DAEBwfj5MmTiIuLQ35+PsaPH68zxsaNG2FpaYnk5GSsXr26wXhaQm1tLW7fvo3a2lqjHZOaF2toPGLMtRhjFiPm2TSwjkQC0Rhg+/btmoEDB2qcnJw0Tk5OmoEDB2p+/vlnQ4ZqFbKzszUANNnZ2fWeq6ys1KSmpmoqKyt1tufl5Rn1R1/btm3TODo6aqytrTUDBgzQzJ8/X5OSkqKzDwDNP//5T51t/fr100yfPl2j0Wg033zzjaZr164atVqtfb66ulpjY2Oj2bt3r0aj0Wg6deqk2bx5s84Yixcv1oSGhmo0Go1mzZo1GoVCoSkoKGgwzgULFmhsbW01JSUl2m2vv/66pl+/fhqNRqOpqqrS2Nraan7//Xed1z333HOaiRMnajQajWbs2LGaZ599tkl5OXbsmAaAZvv27ffdb9++fRozMzNNVlaWdtuFCxc0ADTHjx+/Z+wREREaX19fjUql0m7r2rWrZtmyZdrHADRvvfWWpqCgQFNbW6s5cuSIBoBm3bp12n2+//57jbW1tfbxggULNA899JBGo9FoCgoKNAA0iYmJDcauUCg0GzZsaPC59evXa+zt7bWPBwwYoHnhhRd09nnyySc1Y8aM0Yn37bff1j4uKyvTANDs2bNHo9HcrffIkSN1xqj7TKWnp2s0Go1myJAhmuDg4AZjqnOvz9qDKiws1GzZskVTWFjYrOOS8bCGxiPGXIsxZjESIs8t8fdRW8fPC7VG9+vFTIVBS4Y9/vjjOHz4MAoKClBQUIDDhw8jKiqqWb4EoOYRExODP//8E7/++itGjRqFxMRE9O7dGxs2bNDZLzQ0tN7jujPdKSkpuHLlChQKhfbsuZOTE6qqqpCRkYHy8nJkZGTgueee0z4vl8vx/vvvay+NPnv2LIKDg+Hk5HTPWH19faFQKLSP3d3dcfPmTQDAlStXUFFRgfDwcJ1jbNq0SXuM6dOn44cffkCvXr0wb948/P777/c8lqaJ62KnpaXBy8tL55aJwMBAODg4aPPTUOyurq4IDAyEVCrV2Vb3fur07NlT5/mGtlVVVaGkpKRebE5OTpg6dSoiIiIwduxYfP7558jNzdU+P2fOHDz//PMYMWIEPvjgA53L1Bt6nwMHDtTZNnDgQJ33CABBQUHa32UyGezs7LTvKSUlBQcOHNCpT7du3QBA59h9+vS5ZxxERERERKZK74nUSDysra0RHh6O8PBwvPPOO3j++eexYMECTJ06tUmvLysrQ58+ffDdd9/Ve87Z2RllZWUAgP/+97/o16+fzvNmZmYA0KRJu/5+6bNEItFefl53jN9++w0dOnTQ2a9uEpDRo0fj+vXr2L17N+Lj4zF8+HDExsbi448/rnesLl26QCKRNNtkaQ3Ffr/309Dr6i4Lb2jb319XZ/369Zg5cybi4uKwZcsWvP3224iPj0f//v2xcOFCPPXUU/jtt9+wZ88eLFiwAD/88AMef/zxZn2ff63R2LFj8eGHH9Z7nbu7u/b3hi7nJyIiIiIydU060+3k5KSdFMnR0RFOTk73/KHWKzAwEOXl5Trb/jpxWN3jgIAAAEDv3r1x+fJluLi4oHPnzjo/9vb2cHV1hYeHB65evVrv+Y4dOwK4e4b07Nmz2vt/DYnZysoKWVlZ9Y7x17PQzs7OmDJlCr799lt89tln+M9//tPgeE5OToiIiMDKlSvr5QK4e38yAAQEBCA7OxvZ2dna51JTU1FUVITAwECD3ktzCw4Oxvz58/H777+jR48e2Lx5s/Y5f39/zJ49G/v27cO4cePuObFcQEBAvWXkkpOT9XqPvXv3xoULF+Dr61uvRmy0iYiIiKita9KZ7hUrVmgvoV2xYkWTlzkiYRQUFODJJ5/EtGnTEBQUBIVCgZMnT2L58uX1bgP48ccfERISgkGDBuG7777D8ePHsW7dOgB3J+T66KOPEBUVhffeew+enp64fv06tm/fjnnz5sHT0xOLFi3CzJkzYW9vj1GjRqG6uhonT57EnTt3MGfOHEycOBFLly5FdHQ0li1bBnd3d5w5cwYeHh71Lm1viEKhwGuvvYbZs2dDrVZj0KBBKC4uRnJyMuzs7DBlyhS8++676NOnD7p3747q6mrs2rVL+8VBQ1auXImBAweib9++eO+99xAUFASlUon4+HisWrUKaWlpGDFiBHr27IlJkybhs88+g1KpxIwZMzBkyBCEhIQ8WIH+z18vQddHZmYm/vOf/+Cxxx6Dh4cH0tPTcfnyZUyePBmVlZV4/fXX8cQTT6Bjx47IycnBiRMnEBMT0+BYr7/+OsaPH4/g4GCMGDECO3fuxPbt27F///4mxxMbG4v//ve/mDhxIubNmwcnJydcuXIFP/zwA9auXau96kEoEokEVlZW/O+WiLGGxiPGXIsxZjFink0D60gkjCY13VOmTNH+3tRLk0k4crkc/fr1w4oVK5CRkYHa2lp4eXnhhRdeqLeW+qJFi/DDDz9gxowZcHd3x/fff689y2lra4uDBw/ijTfewLhx41BaWooOHTpg+PDhsLOzAwA8//zzsLW1xUcffYTXX38dMpkMPXv2xKuvvgoAsLS0xL59+zB37lyMGTMGSqUSgYGBWLlyZZPfz+LFi+Hs7Ixly5bh6tWrcHBwQO/evbXvxdLSEvPnz8e1a9dgY2ODRx55BD/88MM9x/Pz88Pp06exZMkSzJ07F7m5uXB2dkafPn2watUqAHf/T+mXX37BK6+8gsGDB0MqlWLUqFH497//3eS478fMzKze0l1NZWtri4sXL2Ljxo0oKCiAu7s7YmNj8dJLL0GpVKKgoACTJ09Gfn4+2rdvj3HjxmHRokUNjhUdHY3PP/8cH3/8MWbNmoWOHTti/fr1GDp0aJPj8fDwQHJyMt544w2MHDkS1dXV8PHxwahRowz+YqE5OTg4cM4JkWMNjUeMuRZjzGLEPJsG1pFIGBJNU2eW+j9mZmbIzc2Fi4uLzvaCggK4uLhApVI1a4DGkJOTAy8vL2RnZ8PT01PnuaqqKmRmZqJjx46wtrYWKMKWIZFIsGPHDkRHRwsdCpFJf9aIiEh/+fn5je5TNxlpaxiXiAxzv17MVOh9GupePXp1dTUsLS0fOCCitkCpVKK4uBhKpVLoUExecXExdu/ejeLiYqFDIQOxhsYjxlyLMWYxYp5NA+tIJIwmz17+xRdfALh7dnTt2rWQy+Xa51QqFQ4ePKhdJoiIGifGq0LESK1Wo6ys7J4zwVPrxxoajxhzLcaYxYh5Ng2sI5Ewmtx0r1ixAsDdM92rV6/WmRzJ0tISvr6+WL16dfNHSC1GzzsLiIiIiIiISE9NbrozMzMBAGFhYdi+fTscHR1bLCgiIiIiIiIiU9DkprvOgQMHWiIOIiIiIiIiIpOjd9MN3J1h7tdff0VWVhZqamp0nvv000+bJbDWhpdiU3OSSqVQKBStYkmt1qKlPmNyuRyDBw/WmYeCxIU1NB4x5lqMMYsR82waWEciYejddCckJOCxxx6Dn58fLl68iB49euDatWvQaDTo3bt3S8QoqLp712tqamBjYyNwNGQqpFIpG+6/qfsC76/zRTQHCwsLuLm5NeuYZFysofGIMddijFmMmGfTwDoSCUPvpnv+/Pl47bXXsGjRIigUCvz0009wcXHBpEmTMGrUqJaIUVDm5uawtbXFrVu3YGFhwUaJmoVarUZNTQ0sLS35bwp383Hr1i3Y2trC3NygC3DuqbKyEhkZGejUqRO/OBMp1tB4xJhrMcYsRsyzaWAdiYSh91+3aWlp+P777+++2NwclZWVkMvleO+99xAVFYXp06c3e5BCkkgkcHd3R2ZmJq5fvy50OGQi1Go1KisrYWNjw6b7/0ilUnh7e0MikTTruFVVVUhNTUWHDh34B4ZIsYbGI8ZcizFmMWKeTQPrSCQMvZtumUymvQzU3d0dGRkZ6N69OwDg9u3bzRtdK2FpaYkuXbrUu3+dyFDFxcX4/fffMWDAANjb2wsdTqvAs/5EREREZIr0brr79++Pw4cPIyAgAGPGjMHcuXNx/vx5bN++Hf3792+JGFsFqVQKa2trocMgE1FZWYna2lpYWlry3xURERERkQnTu+n+9NNPUVZWBgBYtGgRysrKsGXLFnTp0sVkZy4nIiIiIiIiMoTeTbefn5/2d5lMhtWrVzdrQERtgYWFBby9vWFhYSF0KCaPuRY/1tB4xJhrMcYsRsyzaWAdiYQh0ei5OO6JEyegVqvRr18/ne3Hjh2DmZkZQkJCmjVAY8jJyYGXlxeys7Ph6ekpdDhEREREbV5+fn6j+7i6uraacYnIMG2hF9N71qLY2FhkZ2fX237jxg3ExsY2S1BEpk6lUqG0tBQqlUroUEwecy1+rKHxiDHXYoxZjJhn08A6EglD76Y7NTUVvXv3rrc9ODgYqampzRIUkakrKSnBnj17UFJSInQoJo+5Fj/W0HjEmGsxxixGzLNpYB2JhKF3021lZdXgZTm5ubkwN9f7FnEiIiIiIiIik6V30z1y5EjMnz8fxcXF2m1FRUV46623EB4e3qzBEREREREREYmZ3qemP/74YwwePBg+Pj4IDg4GAJw9exaurq745ptvmj1AIiIiIiIiIrHSu+nu0KEDzp07h++++w4pKSmwsbHBs88+i4kTJ3L5ASIiIiIiIqK/0HvJMFPUFqapJyIiIhITLhlG1Da0hV7MoJnPMjIy8NlnnyEtLQ0AEBgYiFmzZqFTp07NGhwRERERERGRmOk9kdrevXsRGBiI48ePIygoCEFBQTh27Bi6d++O+Pj4loiRyOSUlJQgISGBS3YYAXMtfqyh8Ygx12KMWYyYZ9PAOhIJQ+8z3W+++SZmz56NDz74oN72N954gzOYEzWBSqVCQUEBVCqV0KGYPOZa/FhD4xFjrsUYsxgxz6aBdSQSht5nutPS0vDcc8/V2z5t2jSkpqY2S1BEREREREREpkDvptvZ2Rlnz56tt/3s2bNwcXFpjpiIiIiIiIiITILel5e/8MILePHFF3H16lUMGDAAAJCcnIwPP/wQc+bMafYAiYiIiIiIiMRK76b7nXfegUKhwCeffIL58+cDADw8PLBw4ULMnDmz2QMkMkW2trbo168fbG1thQ7F5DHX4scaGo8Ycy3GmMWIeTYNrCORMB5one7S0lIAgEKhaLaAhNAW1oYjIiIiEhOu003UNrSFXkzve7rff/99ZGZmArjbbIu94SYSQlVVFS5fvoyqqiqhQzF5zLX4sYbGI8ZcizFmMWKeTQPrSCQMvZvuH3/8EZ07d8aAAQPw1Vdf4fbt2y0RF5FJq6ysxJkzZ1BZWSl0KCaPuRY/1tB4xJhrMcYsRsyzaWAdiYShd9OdkpKCc+fOYejQofj444/h4eGByMhIbN68GRUVFQYH8sEHH0AikeDVV1/VbquqqkJsbCzatWsHuVyOmJiYepcEZWVlITIyEra2tnBxccHrr78OpVJpcBxEREREREREzUXvphsAunfvjqVLl+Lq1as4cOAAfH198eqrr8LNzc2gIE6cOIE1a9YgKChIZ/vs2bOxc+dO/Pjjj0hKSsKff/6JcePGaZ9XqVSIjIxETU0Nfv/9d2zcuBEbNmzAu+++a1AcRERERERERM1J79nL/04mk8HGxgaWlpbaidX0UVZWhkmTJuG///0v3n//fe324uJirFu3Dps3b8awYcMAAOvXr0dAQACOHj2K/v37Y9++fUhNTcX+/fvh6uqKXr16YfHixXjjjTewcOFCWFpaPujbIyIiIiIiolakpKQE+/fvx5UrV1BbWwsnJydERUXBw8MDAKDRaJCYmIjTp0+jqqoKXl5eiIyMRLt27QSJ16Az3ZmZmViyZAm6d++OkJAQnDlzBosWLUJeXp7eY8XGxiIyMhIjRozQ2X7q1CnU1tbqbO/WrRu8vb1x5MgRAMCRI0fQs2dPnRkmIyIiUFJSggsXLtzzmNXV1SgpKdH+GPJlAdGDMDc3h6urK8zNH/h7L2oEcy1+rKHxiDHXYoxZjJhn08A6kimorKzE119/DTMzM0yaNAkzZszAyJEjYW1trd0nOTkZx44dQ2RkJJ5//nlYWlri22+/Few2ZL0/cf3798eJEycQFBSEZ599FhMnTkSHDh0MOvgPP/yA06dP48SJE/Wey8vLg6WlJRwcHHS2u7q6apv7vLy8eks61D2+3xcAy5Ytw6JFiwyKmag5KBQKDBkyROgw2gTmWvxYQ+MRY67FGLMYMc+mgXUkU5CcnAx7e3tERUVptzk6Omp/12g0OHbsGAYPHoxu3boBAKKjo/Hxxx/j4sWL6NGjh9Fj1rvpHj58OL7++msEBgY+0IGzs7Mxa9YsxMfH63wrYQzz58/HnDlztI9v3LjxwO+HSB9qtRoqlQpmZmaQSg264ISaiLkWP9bQeMSYazHGLEbMs2lgHak1Ky0tRUlJifaxlZUVrKys6u2Xnp6OTp064ccff8S1a9dgZ2eHkJAQ9OnTBwBQVFSEsrIy+Pn5aV9jbW0NT09PZGdnC9J06/1pW7JkSbM0qKdOncLNmzfRu3dvmJubw9zcHElJSfjiiy+0l77U1NSgqKhI53X5+fnaCdvc3NzqzWZe9/h+k7pZWVnBzs5O+8O1xsnYiouLsWPHDhQXFwsdisljrsWPNTQeMeZajDGLEfNsGlhHas0CAwNhb2+v/Vm2bFmD+925cwcnT56Ek5MTnn76aYSEhCAuLg5nz54FcHfOMODu3GN/JZPJUF5e3qLv4V70PtOtUqmwYcMGJCQk4ObNm1Cr1TrP/+9//2vSOMOHD8f58+d1tj377LPo1q0b3njjDXh5ecHCwgIJCQmIiYkBcPdbjaysLISGhgIAQkNDsWTJEty8eRMuLi4AgPj4eNjZ2fHMNRERERERkUikpqbq3Lbc0Flu4O7l4x4eHhg+fDgAwN3dHTdv3sSpU6fQq1cvY4SqN72b7lmzZmHDhg2IjIxEjx49IJFIDDqwQqGod2pfJpOhXbt22u3PPfcc5syZAycnJ9jZ2eGVV15BaGgo+vfvDwAYOXIkAgMD8cwzz2D58uXIy8vD22+/jdjY2HsWiYiIiIiIiFoXhUIBOzu7Ju3n7Oyss619+/ZIS0sDAMjlcgBAeXm5zhXN5eXl9eYDMxa9m+4ffvgBW7duxZgxY1oiHh0rVqyAVCpFTEwMqqurERERga+++kr7vJmZGXbt2oXp06cjNDQUMpkMU6ZMwXvvvdfisREREREREZFxeXl5oaCgQGdbQUEB7O3tAQAODg6Qy+W4evWq9pbj6upq5OTkICQkxOjxAgY03ZaWlujcuXNLxILExESdx9bW1li5ciVWrlx5z9f4+Phg9+7dLRIPERERERERtR79+/fH119/jUOHDqF79+64ceMGTp8+jUcffRQAIJFI0K9fPxw6dAjt2rWDg4MDDhw4AIVCoZ3N3NgkGo1Go88LPvnkE1y9ehVffvmlwZeWtzY5OTnw8vJCdnY2PD09hQ6H2gC1Wo2amhpYWlpy9tAWxlyLH2toPGLMtRhjFiMh8vz3yXIbYsiloi01rhjw80KtkSG92KVLl5CQkICCggI4Ojqif//+2tnLgbv3fScmJuLUqVOoqqqCt7c3IiMj0a5du5Z6G/el95nuw4cP48CBA9izZw+6d+8OCwsLnee3b9/ebMERmSqpVGr0pfLaKuZa/FhD4xFjrsUYsxgxz6aBdSRT4e/vD39//3s+L5FIEBYWhrCwMCNGdW96f8Xl4OCAxx9/HEOGDEH79u11pnWvu46eiO6vrKwMhw8f1i5pQC2HuRY/1tB4xJhrMcYsRsyzaWAdiYSh95nu9evXt0QcRG1KbW0t/vzzT3Tv3l3oUEwecy1+rKHxiDHXYoxZjJhn08A6EgmDN3MQERERERERtZAmn+kODg5u0sRpp0+ffqCAiIiIiIiIiExFk5vu6OjoFgyDiIiIiIiIyPQ0uelesGBBS8ZB1KbY2NjgoYcego2NjdChmDzmWvxYQ+MRY67FGLMYMc+mgXUkEobe63SbIq7TTURERNS6cJ1uorahLfRinEiNSAA1NTXIzs5GTU2N0KGYPOZa/FhD4xFjrsUYsxgxz6aBdSQSBptuIgGUl5fjyJEjKC8vFzoUk8dcix9raDxizLUYYxYj5tk0sI5EwmDTTURERERERNRCHqjprqqqaq44iIiIiIiIiEyO3k23Wq3G4sWL0aFDB8jlcly9ehUA8M4772DdunXNHiARERERERGRWOnddL///vvYsGEDli9fDktLS+32Hj16YO3atc0aHJGpMjMzg4ODA8zMzIQOxeQx1+LHGhqPGHMtxpjFiHk2DawjkTD0XjKsc+fOWLNmDYYPHw6FQoGUlBT4+fnh4sWLCA0NxZ07d1oq1hbTFqapJyIiIhITLhlG1Da0hV5M7zPdN27cQOfOnettV6vVqK2tbZagiIiIiIiIiEyB3k13YGAgDh06VG/7tm3bEBwc3CxBEZm6O3fuYNu2baK8MkRsmGvxYw2NR4y5FmPMYsQ8mwbWkUgY5vq+4N1338WUKVNw48YNqNVqbN++Henp6di0aRN27drVEjESmSS1Wi10CG0Gcy1+rKHxiDHXYoxZjJhn08A6Ehmf3me6o6KisHPnTuzfvx8ymQzvvvsu0tLSsHPnToSHh7dEjERERERERESipNeZbqVSiaVLl2LatGmIj49vqZiIiIiIiIiITIJeZ7rNzc2xfPlyKJXKloqHiIiIiIiIyGTovWRYVFQUxo0bhylTprRUTEbXFqapp9ZFqVSivLwcMpkM5uZ6T61AemCuxY81NB4x5lqMMYuREHnmkmHNj58Xao3aQi+m96dt9OjRePPNN3H+/Hn06dMHMplM5/nHHnus2YIjMlXm5uawt7cXOow2gbkWP9bQeMSYazHGLEbMs2lgHYmEoXfTPWPGDADAp59+Wu85iUQClUr14FERmbjy8nKkpqYiMDCw3hdX1LyYa/FjDY1HjLkWY8xixDybBtaRSBh6z16uVqvv+cOGm6hpampqkJmZiZqaGqFDMXnMtfixhsYjxlyLMWYxYp5NA+tIJAy9m24iIiIiIiIiahq9Ly9/77337vv8u+++a3AwRERERERERKZE76Z7x44dOo9ra2uRmZkJc3NzdOrUiU03ERERERER0f/Ru+k+c+ZMvW0lJSWYOnUqHn/88WYJisjUWVlZoVu3brCyshI6FJPHXIsfa2g8Ysy1GGMWI+bZNLCORMLQe53uezl//jzGjh2La9euNcdwRtUW1oYjIiIiEhOu003UNrSFXqzZJlIrLi5GcXFxcw1HZNJqa2tx8+ZN1NbWCh2KyWOuxY81NB4x5lqMMYsR82waWEciYeh9efkXX3yh81ij0SA3NxfffPMNRo8e3WyBEZmysrIyJCYmIjw8HI6OjkKHY9KYa/FjDY1HjLkWY8xixDybBtaRSBh6N90rVqzQeSyVSuHs7IwpU6Zg/vz5zRYYERERERHdxcviicRL76Y7MzOzJeIgIiIiIiIiMjkPfE93SUkJfv75Z6SlpTVHPEREREREREQmQ++me/z48fjyyy8BAJWVlQgJCcH48eMRFBSEn376qdkDJDJFEokENjY2kEgkQodi8phr8WMNjUeMuRZjzGLEPJsG1pFIGHovGebm5oa9e/fioYcewubNm7FgwQKkpKRg48aN+M9//tPgOt6tXVuYpp6IiIhITLhkmC6xxk3UmLbQi+l9pru4uBhOTk4AgLi4OMTExMDW1haRkZG4fPlyswdIREREREREJFZ6N91eXl44cuQIysvLERcXh5EjRwIA7ty5A2tr62YPkMgUFRUVYefOnSgqKhI6FJPHXIsfa2g8Ysy1GGMWI+bZNLCORMLQe/byV199FZMmTYJcLoePjw+GDh0KADh48CB69uzZ3PERmSSNRoPKykroeXcHGYC5Fj/W0HjEmGsxxixGzLNpYB2JhKF30z1jxgz07dsX2dnZCA8Ph1R692S5n58f3n///WYPkIiIiIiIiEis9G66ASAkJAQhISEAAJVKhfPnz2PAgAFwdHRs1uCIiIiIiIiIhFJdXY3MzEy0a9cOzs7OBo2h9z3dr776KtatWwfgbsM9ZMgQ9O7dG15eXkhMTDQoCCIiIiIiIiKh/fjjjzh+/DgAoLa2Fv/5z3/w448/YvXq1UhNTTVoTL2b7m3btuGhhx4CAOzcuROZmZm4ePEiZs+ejX/9618GBUHU1sjlcgwdOhRyuVzoUEwecy1+rKHxiDHXYoxZjJhn08A6EjXu+vXr8Pb2BgBcvHgRAPDmm29i1KhROHTokEFj6t103759G25ubgCA3bt348knn4S/vz+mTZuG8+fPGxQEUVtjYWEBFxcXWFhYCB2KyWOuxY81NB4x5lqMMYsR82waWEeixlVXV8PGxgYAcOXKFQQEBMDCwgL+/v4oKCgwaEy9m25XV1ekpqZCpVIhLi4O4eHhAICKigqYmZkZFARRW1NRUYFz586hoqJC6FBMHnMtfqyh8Ygx12KMWYyYZ9PAOhI1zs7ODjk5OaipqcGVK1fQqVMnAEBlZSXMzQ2aEk3/pvvZZ5/F+PHj0aNHD0gkEowYMQIAcOzYMXTr1s2gIIjamurqaly8eBHV1dVCh2LymGvxYw2NR4y5FmPMYsQ8mwbWkahx/fv3x/bt27FixQooFAr4+voCuHvZuaurq0Fj6t2qL1y4ED169EB2djaefPJJWFlZAQDMzMzw5ptvGhQEERERERERkdAefvhhdOjQAcXFxejUqRMkEgkAwNHREWFhYQaNadD58SeeeAIAUFVVpd02ZcoUgwIgIiIiIiIiai08PDzg6uqKO3fuwMnJCVKpFP7+/gaPp/fl5SqVCosXL0aHDh0gl8tx9epVAMA777yjXUqMiIiIiIiISGxqa2vxyy+/YMmSJfjqq69QXFwM4O4k4ocPHzZoTL2b7iVLlmDDhg1Yvnw5LC0ttdt79OiBtWvXGhQEUVtjaWmJjh076nyGqGUw1+LHGhqPGHMtxpjFiHk2DawjUeP279+P/Px8TJ06VWfiND8/P1y4cMGgMfVuujdt2oT//Oc/mDRpks5s5Q899JB2HTMiuj+ZTIaHH34YMplM6FBMHnMtfqyh8Ygx12KMWYyYZ9PAOhI1Lj09HWPGjIG3t7f2fm4AcHFxQWFhoUFj6t1037hxA507d663Xa1Wo7a21qAgiNoapVKJ4uJiKJVKoUMxecy1+LGGxiPGXIsxZjFink0D60jUuPLy8ga/mKqpqdFpwvWh90RqgYGBOHToEHx8fHS2b9u2DcHBwQYFQdTWlJaWIj4+HuHh4XB0dBQ6HJPGXIsfa2g8Ysy1GGMWI+bZNLCOZAoSExORlJSks61du3Z4+eWXAdz9cmnv3r24cOEClEolOnfujDFjxkAulzdpfA8PD1y6dAn9+vXT2X769Gl4enoaFLPeTfe7776LKVOm4MaNG1Cr1di+fTvS09OxadMm7Nq1y6AgiIiIiIiIiJrC2dkZkydP1j6WSv//BdxxcXG4fPmydnnrPXv2YOvWrZg2bVqTxh4+fDi+++473Lp1C2q1GseOHcOtW7eQnZ2NqVOnGhSv3peXR0VFYefOndi/fz9kMhneffddpKWlYefOnQgPDzcoCCIiIiIiIqKmkEqlkMvl2h9bW1sAd5e0PnPmDCIiItCxY0d4eHggKioK2dnZyMnJadLY3t7eeOmll6BWq+Hi4oKMjAzIZDI899xz8PDwMChevc50K5VKLF26FNOmTUN8fLxBByQiIiIiIiL6q9LSUpSUlGgfW1lZwcrKqsF9CwsL8cknn8Dc3BxeXl4YPnw47O3tkZubC7VaDT8/P+2+7du3h729PbKzs5t8ebiTkxMee+yxB3tDf6HXmW5zc3MsX76cky8QNYO/XgZDLYu5Fj/W0HjEmGsxxixGzLNpYB2ptQoMDIS9vb32Z9myZQ3u16FDB0RFReHpp59GZGQk7ty5g/Xr16O6uhplZWUwMzODtbW1zmtkMhnKysqaHItGo0FBQQGysrJw/fp1nR9D6H1P9/Dhw5GUlARfX1+DDkhEgKOjI5544gmhw2gTmGvxYw2NR4y5FmPMYsQ8mwbWkVqz1NRUdOjQQfv4Xme5u3Tpov3d1dUVnp6e+Oyzz3DhwgVYWFg8cBw5OTn46aefUFxcDI1Go/OcRCLBu+++q/eYejfdo0ePxptvvonz58+jT58+9aZTb87T8ERERERERGT6FAoF7Ozs9H6dtbU12rVrh8LCQnTq1AkqlQpVVVU6Z7vLy8ubPHv5rl274OHhgaeeegoKhULveBqid9M9Y8YMAMCnn35a7zmJRAKVSvXgURGZuJKSEhw9ehT9+/c36D8u1HTMtfixhsYjxlyLMWYxYp5NA+tIpqimpgaFhYUICgqCu7s7pFIprl69isDAQADA7du3UVxcDC8vryaNV1hYiPHjx8PJyanZYtS76Var1c12cKK2SqVSoaioiF9SGQFzLX6sofGIMddijFmMmGfTwDqSKdi3bx/8/f3h4OCA0tJSJCYmQiqVokePHrC2tkZwcDD27dsHGxsb7ZJhnp6eTZ5ErUOHDigsLBSu6b527Rri4+NRW1uLIUOGoHv37s0WCBEREREREdH9lJSU4KeffkJlZSVsbW3h7e2N5557Tnvb86hRo7B3715s3boVKpUKnTp1QmRk5H3HzM/P1/7et29f7Nu3D2VlZXBxcYGZmZnOvq6urnrH3OSm+8CBA3j00UdRWVl594Xm5vj666/x9NNP631QIiIiIiIiIn01Nhmgubk5IiMjG220/2r16tWQSCQ6E6f98ssv2t/rnmvxidTeeecdhIeHY9WqVbC2tsbbb7+NefPmsekmIiIiIiIi0Zo1a1aLjt/kpvuPP/7A77//Dnd3dwDARx99hDVr1qCgoADt2rVrsQCJTJFMJkNoaGi92f+p+THX4scaGo8Ycy3GmMWIeTYNrCNRwxwcHFp0fGlTdywpKUH79u21j21tbWFjY4Pi4uIWCYzIlFlaWsLLywuWlpZCh2LymGvxYw2NR4y5FmPMYsQ8mwbWkahxhw4dwpkzZ+ptP3PmDA4fPmzQmHpNpLZ3717Y29trH6vVaiQkJOCPP/7QbuM63USNq6qqwvXr1+Hj46OzhiA1P+Za/FhD4xFjrsUYsxgxz6aBdSRq3KlTpxATE1Nvu7OzMw4ePIhBgwbpPaZeTfeUKVPqbXvppZe0v3OdbqKmqaysREpKClxcXPh/ei2MuRY/1tB4xJhrMcYsRsyzaWAdiRpXVlYGuVxeb7tMJkNpaalBYza56eb63ERERERERGTK7O3tkZ2dDUdHR53tWVlZUCgUBo2p15luIiIiIiIiIlPVu3dvxMXFQaVSoWPHjgCAzMxMxMfHIzQ01KAx2XQTERERERERARgwYAAqKiqwe/du7a3T5ubmGDhwIB555BGDxmTTTSQACwsLeHh4wMLCQuhQTB5zLX6sofGIMddijFmMmGfTwDoSNU4ikSA8PBxDhgzBrVu3YGFhAScnJ5ibG946SzQajaYZYxSlnJwceHl5ITs7G56enkKHQ0RERNTm5efnN7qPq6trqxm3pYk1bqLGtNZerLCwEIWFhfDx8YGFhQU0Gg0kEolBY/FMN5EA1Go1ampqYGlpCalUKnQ4Jo25Fj/W0HjEmGsxxixGzLNpYB2JGldRUYFt27YhMzMTEokEr7zyChwdHfHrr7/C2toaEREReo9p0KetqKgIa9euxfz581FYWAgAOH36NG7cuGHIcERtTnFxMX799VcUFxcLHYrJY67FjzU0HjHmWowxixHzbBpYR6LG7d27F1KpFLNnz9a5FaN79+7IyMgwaEy9z3SfO3cOI0aMgL29Pa5du4YXXngBTk5O2L59O7KysrBp0yaDAiEiIiIiIiISUkZGBp5++mnY2dnpbG/Xrh2KiooMGlPvM91z5szB1KlTcfnyZVhbW2u3jxkzBgcPHjQoCCIiIiIiIiKh1dbWNjjZYGVlpcGTqenddJ84cQIvvfRSve0dOnRAXl6eQUEQERERERERCc3b2xspKSk62zQaDZKTk+Hr62vQmHq36lZWVigpKam3/dKlS3B2djYoCCIiIiIiIiKhhYeHY9OmTcjNzYVKpcL+/ftx8+ZNVFZWYtq0aQaNqfeSYc8//zwKCgqwdetWODk54dy5czAzM0N0dDQGDx6Mzz77zKBAhNRap6kn06VWq6FSqWBmZsbZQ1sYcy1+rKHxiDHXYoxZjITIM5cM09UccfPzQq1Ra+zFqqqqcPz4ceTn56Ompgbu7u54+OGHoVAoDBpP7zPdn3zyCZ544gm4uLigsrISQ4YMQV5eHkJDQ7FkyRKDgiBqa6RSKf/PzkiYa/FjDY1HjLkWY8xixDybBtaRqGmsra0xePDgZhtP70+dvb094uPjsXPnTnzxxRd4+eWXsXv3biQlJUEmkzVbYESmrLS0FElJSSgtLRU6FJPHXIsfa2g8Ysy1GGMWI+bZNLCORE1z/fp1bN++HevWrdPeWp2SkoKsrCyDxjP4q65BgwZhxowZmDdvHkaMGGHQGKtWrUJQUBDs7OxgZ2eH0NBQ7NmzR/t8VVUVYmNj0a5dO8jlcsTExNS7tCYrKwuRkZGwtbWFi4sLXn/9dSiVSkPfFpFRKJVK5Ofn89+qETDX4scaGo8Ycy3GmMWIeTYNrCNR41JTU/Htt9/C3Nxce183AFRXV+PQoUMGjWnQnOcnTpzAgQMHcPPmTajVap3nPv300yaP4+npiQ8++ABdunSBRqPBxo0bERUVhTNnzqB79+6YPXs2fvvtN/z444+wt7fHyy+/jHHjxiE5ORkAoFKpEBkZCTc3N/z+++/Izc3F5MmTYWFhgaVLlxry1oiIiIiIiKiNOnToEB599FE89NBDuHDhgna7l5eXwUtk6910L126FG+//Ta6du0KV1dXSCQS7XN//b0pxo4dq/N4yZIlWLVqFY4ePQpPT0+sW7cOmzdvxrBhwwAA69evR0BAAI4ePYr+/ftj3759SE1Nxf79++Hq6opevXph8eLFeOONN7Bw4UJYWlrq+/aIiIiIiIiojbp9+zZ8fHzqbbe2tkZVVZVBY+rddH/++ef4+uuvMXXqVIMOeC8qlQo//vgjysvLERoailOnTqG2tlbn0vVu3brB29sbR44cQf/+/XHkyBH07NlTZ6bGiIgITJ8+HRcuXEBwcHCzxkhERERERESmSy6Xo7CwEA4ODjrbs7Ky4OjoaNCYejfdUqkUAwcONOhgDTl//jxCQ0NRVVUFuVyOHTt2IDAwEGfPnoWlpWW9N+vq6oq8vDwAQF5eXr2lEeoe1+3TkOrqalRXV2sfczIJMjYbGxsEBwfDxsZG6FBMHnMtfqyh8Ygx12KMWYyYZ9PAOhI1rnfv3oiLi8Njjz0G4G6vmJ2djX379hk8o7neTffs2bOxcuXKZluPu2vXrjh79iyKi4uxbds2TJkyBUlJSc0y9r0sW7YMixYtatFjEN2PtbU1unTpInQYbQJzLX6sofGIMddijFmMmGfTwDoSNW7QoEHQaDTYtGkTamtrsX79epibmyM0NBT9+vUzaEy9m+7XXnsNkZGR6NSpEwIDA2FhYaHz/Pbt2/Uaz9LSEp07dwYA9OnTBydOnMDnn3+OCRMmoKamBkVFRTpnu/Pz8+Hm5gYAcHNzw/Hjx3XGq5vdvG6fhsyfPx9z5szRPr5x4wYCAwP1ipvoQVRXVyMvLw9ubm6wsrISOhyTxlyLH2toPGLMtRhjFiPm2TQ0VMe/rwzUkL9fWUpkyiQSCQYPHoyBAweisLAQNTU1cHZ2fqD5wvReMmzmzJk4cOAA/P390a5dO9jb2+v8PCi1Wo3q6mr06dMHFhYWSEhI0D6Xnp6OrKwshIaGAgBCQ0Nx/vx53Lx5U7tPfHw87Ozs7ttEW1lZaZcps7Ozg0KheOC4ifRRUVGBY8eOoaKiQuhQTB5zLX6sofGIMddijFmMmGfTwDoSNZ2ZmRksLS0hl8sfeIJuvc90b9y4ET/99BMiIyMf6MDA3TPOo0ePhre3N0pLS7F582YkJiZi7969sLe3x3PPPYc5c+bAyckJdnZ2eOWVVxAaGor+/fsDAEaOHInAwEA888wzWL58OfLy8vD2228jNjaW38ISERERERGRXtRqNRITE3H8+HHU1NQAuHt1dt++fTFkyBCYmZnpPabeTbeTkxM6deqk94EacvPmTUyePBm5ubmwt7dHUFAQ9u7di/DwcADAihUrIJVKERMTg+rqakREROCrr77Svt7MzAy7du3C9OnTERoaCplMhilTpuC9995rlviIiIiIiIio7di9ezcuXryIESNGwMvLCwCQnZ2NpKQkVFRU4NFHH9V7TL2b7oULF2LBggVYv349bG1t9T7gX61bt+6+z1tbW2PlypVYuXLlPffx8fHB7t27HygOIiIiIiIioj/++AMxMTE6kw66urrC3t4eP/30k3Ga7i+++AIZGRlwdXWFr69vvYnUTp8+rXcQRG2NmZkZ2rVrZ9DlKaQf5lr8WEPjEWOuxRizGDHPpoF1JGqcmZlZvWWrAcDBwcHgz47eTXd0dLRBByKi/8/Ozg7Dhw8XOow2gbkWP9bQeMSYazHGLEbMs2lgHYka17dvXxw8eBBRUVEwN7/bLiuVShw6dAgPP/ywQWPq3XQvWLDAoAMRERERERERtWZ5eXm4evUqVqxYoV0uLz8/HyqVCh07dsSWLVu0+06YMKFJY+q9ZBgRPbg7d+5g69atuHPnjtChmDzmWvxYQ+MRY67FGLMYMc+mgXUkapy1tTUCAwPh7++vXRbb398fAQEBsLa21vlpqiad6XZycsKlS5fQvn17ODo6QiKR3HPfwsLCJh+ciIiIiIiIqLWIiopq9jGb1HSvWLECCoUCAPDZZ581exBEREREREREQqutrQUA7YThRUVFuHjxIpydnQ1eOrtJTfeUKVMwbNgwbN++HVOmTDHoQERERERERESt2Q8//ICAgACEhISgqqoKa9euhZmZGSoqKjBy5EiDJlNr8j3diYmJqKmp0fsARERERERERGKQm5sLb29vAEBqairkcjleffVVREdH4/jx4waNqffs5UT04Ozs7DB69GjY2toKHYrJY67FjzU0HjHmWowxixHzbBpYR6LG1dbWwsrKCgCQkZGBbt26QSKRwNPTE0VFRQaNqVfTnZqairy8vPvuExQUZFAgRG2JmZmZdp4EalnMtfixhsYjxlyLMWYxYp5NA+tI1DgnJydcvHgR3bp1Q0ZGBvr37w8AKC8v1zbj+tKr6R4+fDg0Gk297RKJBBqNBhKJBCqVyqBAiNqSsrIy/PHHH+jRowfkcrnQ4Zg05lr8WEPjEWOuxRizGDHPpoF1JGrckCFD8NNPP2Hv3r3o2LEjvLy8ANw96+3u7m7QmHo13ceOHYOzs7NBByKi/6+2thZZWVno2rWr0KGYPOZa/FhD4xFjrsUYsxgxz6aBdSRqXGBgILy9vVFaWgo3Nzftdj8/PwQEBBg0pl5Nt7e3N1xcXAw6EBEREREREVFrJ5fL610N0qFDB4PHa/Ls5URERERERESknyY33UOGDIGlpWVLxkJERERERERkUpp8efmBAwdaMg6iNsXa2hqBgYGwtrYWOhSTx1yLH2toPGLMtRhjFiPm2TSwjkTC4OXlRAKwsbFBjx49YGNjI3QoJo+5Fj/W0HjEmGsxxixGzLNpYB2JGrZmzRpUVlYCAJKSklBbW9us47PpJhJAbW0t8vLymv0DTfUx1+LHGhqPGHMtxpjFiHk2DawjUcNu376t/VwkJSWhpqamWcdvUtNdUlLSrAclauvKyspw8OBBlJWVCR2KyWOuxY81NB4x5lqMMYsR82waWEeihrm5ueGXX35BYmIiNBoNfv/9dyQlJTX4Y4gm3dPt6OiI3NxcuLi4YNiwYdi+fTscHBwMOiARERERERFRaxEVFYXExERcvnwZEokEV65cgVTa8PnpIUOG6D1+k5puuVyOgoICuLi4IDExkZekEBERERH9TX5+fqP7uLq6GiESItJH+/bt8cQTTwAAFi1ahMmTJ0MmkzXb+E1qukeMGIGwsDAEBAQAAB5//PF7Lh/2v//9r9mCIyIiIiIiIjKWBQsWNPuYTWq6v/32W2zcuBEZGRlISkpC9+7dYWtr2+zBELUVUqkUcrn8npetUPNhrsWPNTQeMeZajDGLEfNsGlhHMkWHDx9GQkIC+vXrh1GjRgEAlEol9u7diwsXLkCpVKJz584YM2YM5HJ5k8YsLCzE0aNHcfv2bQCAs7Mz+vXrBycnJ4NilGg0Go0+LwgLC8OOHTtM6p7unJwceHl5ITs7G56enkKHQ0RERNTmtdSl2i15CbgYx+Yl8SS0B+nFbty4gW3btsHKygq+vr7apnvXrl24fPkyoqOjYWVlhT179kAikWDatGmNjnnlyhX88MMPcHNzg5eXFwAgOzsbeXl5mDhxIjp16qT3e2zSme6/OnDggPb3un5dIpHofWAiIiIiIiIiQ9TU1GD79u0YO3YsDh48qN1eVVWFM2fOICYmBh07dgRwd6K0lStXIicnp9HGPiEhAf3798eIESN0tu/fvx/79+83qOk26NqSTZs2oWfPnrCxsYGNjQ2CgoLwzTffGDIUUZtUVFSEX375BUVFRUKHYvKYa/FjDY1HjLkWY8xixDybBtaRTMnu3bvRpUsX+Pn56WzPzc2FWq3W2d6+fXvY29sjOzu70XFv3bqF4ODgetuDg4Nx69Ytg2LV+0z3p59+infeeQcvv/wyBg4cCODudfT//Oc/cfv2bcyePdugQIjaEo1Gg+rqauh5dwcZgLkWP9bQeMSYazHGLEbMs2lgHak1Ky0tRUlJifaxlZUVrKysGtz3jz/+QG5uLl544YV6z5WVlcHMzAzW1tY622UyWZPWqJfJZMjLy0O7du10tufl5Rk8o7neTfe///1vrFq1CpMnT9Zue+yxx9C9e3csXLiQTTcRERERCSL1dmmj+7RrdA8iEkJgYKDO4wULFmDhwoX19isuLkZcXByeeeYZmJvr3c42qnfv3ti1axfu3Lmjc093cnIy+vfvb9CYekeZm5uLAQMG1Ns+YMAA5ObmGhQEERERERERtV2pqano0KGD9vG9znLn5uaivLwca9as0W7TaDS4fv06jh8/jqeffhoqlQpVVVU6Z7vLy8ubNHv54MGDYWlpiSNHjiAhIQEAoFAoMGTIEPTr18+g96Z30925c2ds3boVb731ls72LVu2oEuXLgYFQURERERERG2XQqGAnZ1do/t17NgR06dP19n2yy+/oH379hg4cCDs7OwglUpx9epV7dnz27dvo7i4WHvm+n4kEglCQ0MRGhqK6upqAPf+AqCp9G66Fy1ahAkTJuDgwYPae7qTk5ORkJCArVu3PlAwRG2FXC7HsGHDmrxWIBmOuRY/1tB4xJhrMcYsRsyzaWAdyRRYWVnBxcVFZ5uFhQVsbGy024ODg7Fv3z7Y2Nholwzz9PTUe0myB2226+jddMfExODYsWNYsWIFfv75ZwBAQEAAjh8/3uAsb0RUn4WFBdq3by90GG0Ccy1+rKHxiDHXYoxZjJhn08A6UlsxatQo7N27F1u3boVKpUKnTp0QGRkpWDwG3Xnep08ffPvtt80dC1GbUVFRgUuXLsHf3x+2trZCh2PSmGvxYw2NR4y5FmPMYsQ8mwbWkUzV1KlTdR6bm5sjMjJS0Eb7rwxap5uIHkx1dTUuXbqkvU+EWg5zLX6sofGIMddijFmMmGfTwDoSCYNNNxEREREREbV5KpUKmzZtQkFBQbOOy6abiIiIiIiI2jwzMzPk5+c3+7hsuomIiIiIiIgA9OzZE2fOnGnWMQ2aSA0Arly5goyMDAwePBg2NjbQaDSQSCTNGRuRybK0tESnTp1gaWkpdCgmj7kWP9bQeMSYazHGLEbMs2lgHYkap1arcfLkSVy9ehXu7u71Pi8RERF6j6l3011QUIAJEybgf//7HyQSCS5fvgw/Pz8899xzcHR0xCeffKJ3EERtjUwmQ58+fYQOo01grsWPNTQeMeZajDGLEfNsGlhHosbdunUL7u7uAIDCwsJmGVPvpnv27NkwNzdHVlYWAgICtNsnTJiAOXPmsOkmagKlUonS0lIoFAqYmxt8wQk1AXMtfqyh8Ygx12KMWYyYZ9PAOhI1bsqUKc0+pt73dO/btw8ffvghPD09dbZ36dIF169fb7bAiExZaWkp4uPjUVpaKnQoJo+5Fj/W0HjEmGsxxixGdXnOyMhASUmJ0OGQgfh5IWq6wsJCXLlyBbW1tQAAjUZj8Fh6N93l5eWwtbVtMCgrKyuDAyEiIiKi1uP333/H5cuXAdxdRgcAzp07h7179yInJ0fI0IiIWkxFRQU2bdqEf//739i8eTPKysoAAL/++iv27t1r0Jh6N92PPPIINm3apH0skUigVquxfPlyhIWFGRQEEREREbUut2/fhrOzMwDg5s2bAICwsDAEBwcjNTVVyNCIiFrM3r17IZVKMXv2bFhYWGi3d+/eHRkZGQaNqffNHMuXL8fw4cNx8uRJ1NTUYN68ebhw4QIKCwuRnJxsUBBERERE1LrU1tZqZ+0tKCgAcHcNW3d3d5w7d07I0IiIWkxGRgaefvpp2NnZ6Wxv164dioqKDBpT7zPdPXr0wKVLlzBo0CBERUWhvLwc48aNw5kzZ9CpUyeDgiBqiziBifEw1+LHGhqPGHMtxpjFwMbGBgUFBVAqlbh9+zak0rt/NtbW1mp/J/Hh54Xo/mpra3XOcNeprKw0+PNj0Kvs7e3xr3/9y6ADEhHg6OiIcePGCR1Gm8Bcix9raDxizLUYYxYLf39/HD16FObm5pDJZAgPD9cuF2tvby90eGQAfl6IGuft7Y2UlBQMGzZMu02j0SA5ORm+vr4Gjal30x0XFwe5XI5BgwYBAFauXIn//ve/CAwMxMqVK+Ho6GhQIERERETUenTu3BlOTk6oqKiAq6srJBIJgLtrPffs2VPg6IiIWkZ4eDg2bdqE3NxcqFQq7N+/Hzdv3kRlZSWmTZtm0Jh6Xxv0+uuva5eKOH/+PObMmYMxY8YgMzMTc+bMMSgIoramuLgYcXFxKC4uFjoUk8dcix9raDxizLUYYxYTJycneHp6ory8HHv27EFxcTE8PDzQvn17oUMjA/DzQtQ4FxcXvPzyy/Dy8kLXrl1RU1ODgIAAvPTSS3BycjJoTL3PdGdmZiIwMBAA8NNPP2Hs2LFYunQpTp8+jTFjxhgUBFFbo1arUVJSArVaLXQoJo+5Fj/W0HjEmGsxxiwm165dQ3p6OkpLS6FWq5GcnIzAwECDL7EkYfHzQtQ01tbWGDx4cLONp3fTbWlpiYqKCgDA/v37MXnyZAB3vwmtOwNOREREROKWnp6OP/74A507d4avry9SUlLQrl07nDp1CtXV1ejatavQIRIRtYjKykqcOXMGt27dAgA4OzsjODgYNjY2Bo2nd9M9aNAgzJkzBwMHDsTx48exZcsWAMClS5fg6elpUBBERERE1LpcuXIFffr0ga+vL+7cuQPg7uRqrq6uuHDhAptuIjJJ169fx/fffw8rKyt4eHgAAI4fP46DBw9i4sSJ8PHx0XtMvZvuL7/8EjNmzMC2bduwatUqdOjQAQCwZ88ejBo1Su8AiIiIiKj1qaysRLt27eptb9euHSorKwWIiIio5e3evRvdu3dHZGSkdnlEtVqN3377Dbt378b06dP1HlPvptvb2xu7du2qt33FihV6H5yorZLJZBg4cCBkMpnQoZg85lr8WEPjEWOuxRizWMjlcmRnZyMwMFAnz1euXIFCoRA6PDKAKX1e8vPzG93H1dXVCJGQqSksLMSTTz6pbbgBQCqVIjQ0FGvWrDFoTMNW9/4/VVVVqKmp0dlmZ2f3IEMStQmWlpbaq0SoZTHX4scaGo8Ycy3GmMWiR48eOHLkCG7fvq09452RkYH8/HyEhoYKHB0Zgp8Xosa5u7vj9u3b9VZpuH37tsFf5Oi9ZFh5eTlefvlluLi4QCaTwdHRUeeHiBpXWVmJtLQ0Xp5nBMy1+LGGxiPGXIsxZrHw9PTE8OHDYWlpiZycHGRkZMDMzAwjRozgPD4ixc8LUcPy8/O1P3379kVcXBx+//13ZGVlISsrC7///jv27t2L/v37GzS+3me6582bhwMHDmDVqlV45plnsHLlSty4cQNr1qzBBx98YFAQRG1NVVUVzp8/Dzc3N4NnQaSmYa7FjzU0HjHmWowxi4mTkxP69++PO3fuID4+HoGBgTzJImL8vBA1bPXq1ZBIJNBoNNpt8fHx9fbbvn07evTooff4ejfdO3fuxKZNmzB06FA8++yzeOSRR9C5c2f4+Pjgu+++w6RJk/QOgoiIiIiEV1tb2+B2pVKp/d+6fSwsLIwWFxFRS5o1a1aLjq93011YWAg/Pz8Ad+/fLiwsBHB3KTFDZnIjIiIiotZhx44dkEgk9bbXnf05cOAAAEAikeDJJ580amxERC3FwcGhRcfXu+n28/NDZmYmvL290a1bN2zduhV9+/bFzp07WzxYIiIiImo5Q4cObXB7aWkpTp06hT59+ohm5nKNWo3rWVkoKyuDXC6Hj7c3JFK9pzMiojaotLQUWVlZKC8v17nkHAD69eun93h6N93PPvssUlJSMGTIELz55psYO3YsvvzyS9TW1uLTTz/VOwCitsjCwgKenp68NM8ImGvxYw2NR4y5FmPMrZmLi0uD221tbeHp6QlXV1fI5XIjR6W/i2lp2BMXh5KSEu02Ozs7jB41CgP9fQSMTFj8vBA17uzZs9i1axfMzMxgY2NT7+ofQ5puiebvrbuerl+/jlOnTqFz584ICgp6kKEEk5OTAy8vL2RnZ3M2TiIiIiIAt27duu/zzs7OLXp8Q9ZhTr1diotpadi6dSv+/gdu3Z/N058Yi27duuk1blO15NrRLTW2WNe7FmvcVF9r68VWrFiBPn364JFHHmnwdhtDPNA63QDg4+MDH5+2+40hkSFUKhWqq6thZWUFMzMzocMxacy1+LGGxiPGXIsxZrGou3/77+r+CG2N93Rr1GrsiYur13ADgAZ3G++4uDh07dq12f6YFhN+XogaV1tbix49ejTrfyOafGPLmDFjUFxcrH38wQcfoKioSPu4oKAAgYGBzRYYkSkrKSnBrl27dC57o5bBXIsfa2g8Ysy1GGMWi+joaO1P3b3evXv3hqOjIwYPHixscPdwPSvrvv8WNACKi4uRlZVlvKBaEX5eiBoXHByM1NTUZh2zyWe69+7di+rqau3jpUuXYvz48drJ05RKJdLT05s1OCIiIqK/q66uxh9//IFbt26hsrISwN2zstL/myQrOjpawOhMh6Wlpfb3unuA27VrBzs7O6SkpCA8PFyo0O6prKysSfuVlpa2cCREJFbDhw/H999/jytXrsDFxaXeVSERERF6j9nkpvvvt34/4K3gRERERAY5duwYysrK0LFjR6jValy4cAFdu3aFTCYTOrQ2wdrautU2rU2d5E0sM7ATkfEdPnwYV65cQfv27XHz5s1mucz8ge/pJiIiIjKm27dvY9iwYXBwcMCdO3dw4cIFeHh4wNHRUejQTMpfbyOsa7Jv376NnJycVrtMrI+3N+Ry+T3PeEsA2Nvbw9vb27iBEZFoHDlyBFFRUejVq1ezjdnkplsikdTr8tviBBREREQkLIVCAZVKJXQYJm/fvn2QSCQ6VzeeOXMG7dq1w8MPPyxgZPem0Wh0Lov/q7q/WkeNGsW/YYnonszMzODl5dWsYzZ5yTCpVIrRo0fDysoKALBz504MGzZMeylXdXU14uLiRPl/gq1tmnoyfRqNBmq1GlKplP/H38KYa/FjDY1HLLkuLCzEuXPnEBgYCDs7OwDQiZlrEDeP8vJy7e8ajQYajQY2NjYwNzfOhZKGLAm1attOJCYlwcLCAtZWVij9yxlvezs7jGriOt2tbVmv5hq7oc+4WJfeEmvcVF9r68UOHTqEsrIyjB49utnGbPJ/NadMmaLz+Omnn663z+TJkx88IqI2QCKRcKkOI2GuxY81NB6x5NrCwgK1tbVISkrS2a7RaCCRSFrlUlZiJLZ75I8dO4aDBw8CAB577DF0DwzE1cxMbP7uO6g1GkyePAVO7ZwAVYXAkQpHLJ9xIiH9+eefyMzMxKVLl+Di4qKdpLPOhAkT9B6zyU33+vXr9R6ciBpWWlqKkydPIiQkhJO5tDDmWvxYQ+MRS66PHTsGqVSKfv36Qa1WIz09HV27doWtra3QoZmcmzdvIj09HcXFxaiqqkL79u3RvXt3ODs7Cx2ajrKyMjz99NPoPSoKPXv0QI8ePQAAnTp1QgdPT2RnZyM7J/tu091GVFVVISUlBfn5+doViP56gev48eOFCo2oVbO2tkZAQECzjsmJ1IgEoFQqcevWLSiVSqFDMXnMtfixhsYjllwXFxcjPDwcdnZ2uHPnDoqLi2Fvb8+J1JrZ9evXcfz4cXh6esLLywvp6emQSCRISkrCww8/DB+fxi/TNpbXXnsNV65cwVA7O4wZM0bnOR9vb2RnZ+P69et46KGHBIrQ+I4fP46KigoEBgbCxsYGwN0vJ1JSUtpUHoj0FRUV1exjShvfhYiIiKj1cHR01K7PTS0nNTUVQUFBCA0N1c72HRQUhJ49eyI1NVXg6P6/Xbt2Yc2aNQDurtFu/X8NZh3v//tyIOv6daPHJqTbt2+jX79+6Ny5Mzp06IAOHTrAxcUFALT/S0TGwTPdREREJCpdunTBmTNn0LVrV+29dqWlpdqJoVrrclZiU15eDg8Pj3rbPTw8cP78eQEiqu/mzZt47rnnAACzZ89Gx44d6+3j5eUFCYCCwkKUlZWhnU3bOOfE2y2IDPP555/f9/lZs2bpPSabbiIiIhKVI0eOAABOnDih3Xb06FEA4ERqzcjW1hY3b96sd39/fn5+q2noXnjhBdy8eRM9evTA0qVLcbWstt4+1tbWcHV1RV5+PrKysuDT1df4gQqgV69eOHfuHEJCQkQ3KR6RkPr166fzWK1WIy8vD1euXMGAAQMMGlPvpvvgwYMYMGBAveUilEolfv/9dwwePNigQIjaEltbW4SEhLSaP1pMGXMtfqyh8Ygl15GRkdrfa2pqkJeXBzc3t3uuz0yG8ff3x5kzZ1BUVAR7e3t07NgRly9fRlZWFoKDg4UOD2fOnMGvv/4KS0tLfPvtt7C2tgYaaLoBwMfH527Tff060Eaa7iNHjkClUmH37t0wMzODVCqFRqOBmZkZEhMT8fjjjwsdIlGr1L9//wa3Hz9+HLm5uQaNqXfTHRYWhtzc3Hr3ghQXFyMsLEyU63QTGZuVlRX8/PyEDqNNYK7FjzU0HrHk+q9n7WQyGSdQayGdO3eGtbU1Ll26hOzsbACAnZ0dQkND0aFDB0Fju3PnDuLi4gAA77//fqMTg3n7+ODY8eO43obu624NX4wQmZIuXbogISHBoInW9G6669bA/LuCggJeukLURNXV1bhx4wY6dOgAKysrocMxacy1+LGGxiOmXJeVleHSpUsoKipCdXU12rdvj4CAAMjlcqFDMymenp7w9PRsVf821Go1duzYgZqaGgwZMgRz5sxp9DV1E8Hl5+ejqqrq7llxE+fr61tv21/rSET6SU1N1a4EoK8mN93jxo0DcPdeqalTp+r8B1elUuHcuXMGX+NO1NZUVFTg5MmTcHR0FPyPF1PHXIsfa2g8rTnXN27cgLu7O6RSKfLy8nD48GE4ODhALpfj9u3bkEgkiIuLw6BBg+Dm5iZ0uCalsLAQubm5uHDhAszMzARfKiw5ORnZ2dmwsrLCxo0bYWZm1uhr5HI52jk5oaCwENnZ2ejSpYsRIhWeWq3Gn3/+iZKSEgCAmZkZUlJSWuVnnKi1qFsN4a/KyspQXl6uc3uTPprcdNvb2wO4e6ZboVDodPmWlpbo378/XnjhBYOCICIiIrqf5ORkPPbYY7C2tsa5c+fg7++PoKAg3LlzB1lZWejXrx+ys7Nx7tw5Nt3NpKKiAkePHsXt27e1c/kcO3YMGRkZ6N+/vyD3/ufm5iIxMREAMGbMGL2+APD28UFBYSGysrLaRNNdWlqKQ4cOobKyUjsZXl3zXVFRwdsyiO6ha9euOo8lEglkMhl8fX3Rvn17g8ZsctO9fv16AHcvVXnttdd4KTkREREZzfjx47W/l5SUIDQ0tN4+HTt2xKVLl4wZlkk7efIk1Go1Ro0aBZVKhfj4eAwYMADp6ek4efKk0SfPra2txfbt26FWq9G9e3cEBQXp9XofHx+cOXOmzdzXfebMGcjlcgwfPlx7Vjs/Px9JSUlIT0/nJeZE9zB06NBmH1PvhQoXLFjAhpuIiIiMKjk5GbW1d2emtrKyQlFRUb19ioqK2sS9usZy69Yt9OnTB3Z2dtptMpkMwcHBuHXrltHjiY+Px+3bt6FQKAy6xLPuvu4///wTSqWyucNrdW7duoWgoCCdy8jrZvi/c+eOUGERtUlNOtPdu3dvJCQkwNHREcHBwQ1OpFbn9OnTzRYckakyNzeHs7NzvaX3qPkx1+LHGhpPa861hYWF9nc/Pz+cPHkSZWVlsLW1hb29Pa5fv47MzEz4+/sLGKVpsbGxgVqtBqD7b6OmpsbgyYQMdeXKFe267NHR0QYd39HBAQqFAqqiCty4cUPwe9NbmpmZWb0vF8zNzeHg4IDy8nKBoiJqvRYtWnTfPrfOu+++q/fYTfp/1aioKO23ZNHR0XofhIh0KRQKhIWFCR1Gm8Bcix9raDytOdd9+/bV/h4YGAhzc3NcunQJlZWVAO6u1929e/c2ca+usTz00EM4c+YMevfuDScnJ4SFhaGwsBBnz55tdImu5lRRUYFffvkFANCvXz/Dl7WTSODj44Pconxcv37d5Jtud3d3nDx5Eg8//DCcnJwA3P2caDQaXlpO1IAJEybc87mcnBwcO3YMGo3GoLGb1HQvWLCgwd+JyDAajQZqtRpSqbRJ36iR4Zhr8WMNjUcMuVar1cjKyoKPjw+6du2KmpoaqNVqWFlZtdqYxer48eNQqVRISEjQ5rbu38eJEye0Z56Blj0ps2vXLpSVlcHZ2RkjRox4oLF8vL2Rm9I21usODg7G8ePHkZCQAKn07h2larUaHh4e6NWrl7DBEbVC3bp1q7ft9u3bSEhIQHp6OoKCggy+31vve7pPnDiBY8eO1dt+7NgxnDx5Uq+xli1bhocffhgKhQIuLi6Ijo5Genq6zj5VVVWIjY1Fu3btIJfLERMTg/z8fJ19srKyEBkZCVtbW7i4uOD1119vE/fqkHgVFRXhp59+avCeRGpezLX4sYbGI4ZcS6VSnDp1CiqVCgBQXl6OX3/9tVXHLFbBwcEICQnBww8/jICAAO0EZiEhIejVq5fOT0tJSUlBWloapFIpxo0b98C3Pnj/39nt7Oxs7aXzpsrS0hKDBg3C6NGjMWDAAAwYMAADBw7En3/+ycvLiRpRWlqKX3/9FatWrYJarcY///lPREdHw8HBwaDx9P4vV2xsLObNm4d+/frpbL9x4wY+/PDDBhvye0lKSkJsbCwefvhhKJVKvPXWWxg5ciRSU1O1k7XNnj0bv/32G3788UfY29vj5Zdfxrhx45CcnAzg7hrhkZGRcHNzw++//47c3FxMnjwZFhYWWLp0qb5vj4hMSEVFBaqqqoQOg4iamZOTE4qKijixawvz9fXV/n7nzh1cuHABHh4eRltqqqioCHv27AEAhIWFNctScM7OzrCxsUFlZSXy8vLg4eHxwGO2dgqFAgqFAmq1Gjk5OUKHQ/TATpw4gZMnT2q/bHVxccHgwYO1txcplUrs3bsXFy5cgFKpROfOnTFmzBjI5fJGx66qqsKhQ4dw/PhxuLm5YfLkyc1yK4reTXdqaip69+5db3twcDBSU1P1GisuLk7n8YYNG+Di4oJTp05h8ODBKC4uxrp167B582YMGzYMwN2lywICAnD06FH0798f+/btQ2pqKvbv3w9XV1f06tULixcvxhtvvIGFCxdqZ2kkorZBo9EgNTUVly5dglKp1N57c/XqVfTu3ZuXnxKZgM6dO+Ps2bOoqKjQnvksLS3Vfr4NPRNB9anVavz555/aqwxv3rwJe3t77eXKLUWlUmHHjh2orq6Gt7c3Bg4c2CzjSiQSeHt7Iz09HdevXzfppvvMmTP/j73zjo+qzPr4d0oy6T0hCakkkIQQOgJKEEQUbCAi2Ht7LYvrvusu6uqu77qurrqWdd11rWtHBEUWAoIgodckJARCeu+9Tqa8f8S5m56ZZCYzE57v5zOfT+bOnXtPnpnnzj3POed38PT0ZMKECeh0Ovbu3Ut1dTUAtbW1ok+3wG7x8PDg8ssvl7QKUlJS+PLLL3nwwQcJCAggKSmJ8+fPc+ONN6JSqdi+fTsbNmzgnnvuGfS4Bw4c4MCBA1J2dX/p5sPFZKdbpVJRUVHRR8SirKxsxCk/DQ0NANIAnjhxgs7Ozh71O7GxsYSFhXHo0CHmzZvHoUOHSEhIYNy4cdI+V155Jf/zP/9DRkYGM2bMGJFNAoHAvjh9+jR5eXkkJCTg5+dHY2Mjhw8fprCwEEdHRxISEqxtokAgGCGHDh0CupwKA4cPHwa6nKobb7zRKnaNNZqamkhOTqatrQ0XFxeg6xqbm5tLYmKiUVGj4fKXv/yFwsJCVCoV119/vVkXTMPDwyWnu79+72OF4uJiKUJXVlZGc3MzF198MQcPHiQ7O5uoqCgrWygQDI+YmJgez5csWcLx48cpLi7Gw8ODU6dOccMNNxAZGQl0iYK//fbbFBcXExISMuBxd+3ahYODAz4+PqSmppKamtrvfoMJrg2EyV7yFVdcwfr16/nuu+/w9PQEutJ/nnrqKZYuXWqyAQZ0Oh2PP/44l1xyCVOmTAGgvLwcR0fHPivW48aNo7y8XNqnu8NteN3wWn90dHTQ0dEhPW9qahq23QKBwLbIz89n9uzZkjKrIdI9efJksrKyhNMtEIwBuvdobmhoYP/+/SxYsEC6LxGYh1OnTuHm5saSJUtobW3lhx9+IDExkXPnznHq1CkSExMtdt5nn32W++67j+XLl5s9c8HQr7uwsBC9Xj9mM6A6OjqkvvVlZWWEhoZKJRnNzc3WNE0gMBs6nY4zZ87Q2dlJaGgoZWVl6HS6HgFiPz8/PD09KSoqGtTpnjZtmsWuByY73a+88goLFy4kPDxciiKnpKQwbtw4Pvnkk2Eb8sgjj5Cens7+/fuHfQxjefHFF/nDH/5g8fMIBAPh4eHBNddcI7XiE5gPtVqNh4eH9Nww1mq1mvT0dCtaJhguYr6MHvYy1q2trfj6+iKXy3FycpJslslk1NTUiFpvM1FVVcWSJUtQqVQolUppnFUqFT/++KNFztnW1satt95KZ2cncXFxFmlNFhQUhIODA21tbVRXV+Pv72/2c9gCTk5ONDY24uTkRHl5OTNnzsTDw4NLL72UgwcPWts8gaAPTU1NNDY2Ss8N15v+qKio4P3330ej0eDo6MjatWvx9/envLwchUIhLTgZcHV1HXKxyZJdGEwuyBk/fjxpaWm8/PLLTJ48mVmzZvHGG29w+vRpQkNDh2XEo48+ytatW9mzZ0+P1YfAwEDUanUfRdKKigpJTCMwMLCPmrnh+UCCG+vXr6ehoUF6mFqLLhCMFIVCgYuLCwqFwtqmjDm8vLw4f/689Nww1rm5uSIKZqeI+TJ62MtY7927F7VaDfS0ubOzk71791rXuDGEQqGQusF0H2eNRmOxmu7f/va3ZGZmEhgYyDXXXGORc8jlcumedSy3DouIiODQoUPs2LED6MoEVSgUtLS09FicFvSkoqJiyIfAMkyePBlPT0/p8eKLLw64r5+fHw899BD33Xcfs2fP5ttvv6WqqmoUrTWNYRVhu7q68sADD4z45Hq9nscee4zNmzezd+9eKe/ewKxZs3BwcGD37t3ccMMNAJw7d47CwkKpBmf+/Pm88MILVFZWEhAQAMAPP/yAh4cHkydP7ve8vVdNuq+oCASjQXNzM2lpaUydOtWiNXEXIlOnTmX//v1UVlbi6+tLZ2cn5eXl6PV6Fi5caG3zBMNAzJfRw17G2lA2Aj1t1uv1Nr9gYE8EBQVx/Phx5syZg6OjI2lpaYSFhXHmzBmzCZBptVqSk5MpKyujpKSEN998E4APPvhAqiO3BGFhYeTm5lJQUMDs2bMtdh5rMmXKFDw9PWltbSU0NBSFQkFzczM5OTl9tJkEAlvgzJkzUnkgMGjWlUKhkHTAgoODKS0t5fDhw0yZMgWtVkt7e3uPaHdLS4tVf9eMcrq3bNnC8uXLcXBwYMuWLYPue9111xl98kceeYTPP/+c7777Dnd3d6kG29PTE2dnZzw9Pbn33nt54okn8PHxwcPDg8cee4z58+czb948oKvGfPLkydx+++28/PLLlJeX88wzz/DII4/YfHqc4MKls7OT4uJi4uLirG3KmCMgIIDly5eTnZ1NY2MjnZ2daDQaEhMTx2wK4VhHzJfRw9bH2tAuVCaTcezYMeRyOZ2dnVRWVtLR0UFrayt+fn5WtnLsMGPGDI4ePcru3buRyWTo9XpKSkoYP368WXpzb9q0iXXr1vVpY3XllVeyfPlyi0YUDQJjhYWFFjuHLdA7C7W1tZW6ujrJWREIbAl3d/dhZ2Ho9Xq0Wi1BQUHI5XJyc3OlAGx1dTUNDQ3Dzso2B0Y53StXrqS8vJyAgIBBc91lMhlardbok7/zzjsALFq0qMf2Dz/8kLvuuguAv/71r8jlcm644QY6Ojq48sor+fvf/y7tq1Ao2Lp1K//zP//D/PnzcXV15c477+T555832g6BYLRoamqisrJSKpnIycnB2dkZgPj4eCtaNrZwdnaWBNPq6ur44Ycf+tT2CAQC+8PBwQHourlSKpUoFAop6u3o6EhgYKCI4JkRR0dHFixYQFNTE2VlZaSkpHDJJZf0iEQNl02bNrF69eoeWQsGdu7cyaZNm8zWJqw/xo8fj0KhoLGxkfr6+jHZZi4zMxNXV1dJOO7gwYPSAkdTU5NoGSawW3bt2sXEiRPx9PSko6OD06dPk5+fz2233YaTkxMzZsxg586dODs7Sy3DQkJCBhVRszRGOd06na7fv0dKfxfa3jg5OfH222/z9ttvD7hPeHg427ZtM5tdAoElyMnJ4eTJk5IgDXSJ1BhSIYXTbT7UajW1tbV0dHRIohmlpaU0NDQQERFhXeMEAsGwueiii4CuMreYmBiUSiV1dXWUlpYyefJk4URYCHd3d6m22xwp31qtlnXr1g16H/j4449z9OhRiykJOzg4EBwcTFFREQUFBWPS6c7NzWXu3LlAV0efiooKZs6cycmTJ8nKypKccYHA3mhpaWHz5s00NzejUqkYN24ct912m9QGb9myZezYsYMNGzag1WqJiorq0fXCGoyssbZAIDCazMxMpkyZQlxcnBR9nTdvnrhJNDOGmh6NRoODg4N0U3fu3DnkcrlwugWCMUDvHq3QJYjV0dExoIiqwHT0ej3FxcVUVlZK7VVTU1OljIPhRqKTk5P7pJT3Pm9RURGFhYVSGrglCAsLk85jCZV0a9Pe3i4tkhhahvn6+gJCz0hg36xYsWLQ15VKJVdffbXVHe3uGOV0G0QtjOEXv/jFsI0RCMYyarVaqiVxcnIiISFBpDxbgJSUFCIjI0lISECpVNLW1kZ+fj4RERFSKr/AvhDzZfSwl7E+cOAA48ePJzo6GoVCgVKppLCwkJycHKZNm0Z0dLS1TRwTpKSkkJOTQ0BAAI6Ojnh7e6NSqUYsVldWVmbUfgZH31KEh4dz4MCBMatg7uDgQGtrKy4uLpSXlzNlyhScnJyYNGkSubm51jZPILigMMrp/utf/2rUwWQymXC6BYIBCA0Npby8nOjoaJydnW1WqMjeaWtrY+LEiVIKvxhr+6Gzs1OKoHV2dkrblUql5EQZthv2E4yc//znP1x++eWoVKoe80WtVvPDDz/YVKTAQF1dnSTkVV1djaurK1dccQXFxcWkp6cLp9tMFBQUcMkllxAUFGTW4xp7PHd3d7OetzehoaFSb/fm5mabVuwfDiEhIRw+fBh3d3cpC8TBwQEfHx8qKyutbZ5AcEFhlNOdl5dnaTsEgjGPm5sbGRkZ1NbW4urqSmtrK25ublLEYNKkSVa2cGwQGBhIXV2ddPOkVqupqqrC398fR0dHK1snGIxvv/2Wa6+9FicnJzZv3izVcvau+5TJZNx4443WMHFM0tLSIo1x9/mi0+loa2uzsnX9o9VqpYW10tJSPDw86OzsxNfXl9bWVitbN3ZwcHDA1dUVMO+1NDExkZCQEEpKSvqt65bJZISEhFi85tjJyYmAgAAqKiooKioacwu006dPx8XFhba2NqZOnYqDgwNqtZry8nJRaiUQjDIjquk2XCgtJXIhEIwlcnNzUSqVVFVVUV5eLvUPFE63eQkKCiI1NZWGhga8vLxoaWkhNTWVadOm4ebmZhbVXYFluPTSS6Wb+e5dLZqamjhx4gSzZs2yeOTrQqKkpET6u7y8HAcHB5qbm6X50tLSIjlctoabmxslJSWEhIRQWVmJRqMhNjYWvV4vsiDMSHx8PBkZGcyZM4eWlhYOHDjA0qVLR+x0KxQK3njjDVavXt3nNcM95euvvz4q95fh4eFUVFRQUFAw5pxuuVxObGxsj20tLS3k5+ezdOlSK1klEFyYDMvpfv/99/nrX//K+fPnAZg4cSKPP/449913n1mNEwjGEt1TNA1CaomJiUJIzcwcP34cgDNnzvTYnpqaKiKkNk5AQEC/fxucKB8fHzFfzEj3ntdHjx7t8VpaWhpubm42Ky41efJkjhw5QmpqKt7e3tTW1gJQUVExJlWorUVISAiFhYVs2bJFqvM/fPiwtFh8xRVXDPvYq1atYuPGjdx66620t7f3OOfrr7/OqlWrLNqn20B4eDhHjx4ds3XdhlalHR0d6PV6KXslJyeH2bNnW9k6geDCwWSn+9lnn+W1117jscceY/78+QAcOnSIX/7ylxQWFor+2AKBwKqsWbOmx3PDAsfSpUuFw2ZnGFq/GRwqQ9s3QKRGmgHDXOle022YL5dffrlNz5fQ0FD8/Pxob29Hr9eza9cuoGuxRmSzmI+jR49SV1dHeHg4er2e5uZm/P39zSZKuWrVKkJDQzl//jxPPfUUS5cuJTExccRCbaZgSGGvqKiQMtDGCt1blRr+L61WC3S1LBUIBKOHyU73O++8w7/+9S9uvvlmadt1113H1KlTeeyxx4TTLRB0IyUlhSlTpqBUKklJSZG2G1b1z507J/0QGkSBBAJBz9Zvhtrdc+fOIZPJkMlkwuk2I7YolGYMzs7OODs7U1dXJ20ztEMSmIeysjIWLlyIv78/dXV15ObmEhUVZbYFGbVaLaloP/zww1ZZMHFzc8PHx4fa2lqKi4vHlAhf91alBrq3LBUIBKOHyU53Z2dnv+kos2bNQqPRmMUogWCsUFdXh06nk/42oNFoUCgUNDU10dbWJnQRRkhWVhZRUVEoFAqysrJ6vNbe3o5KpaKoqIiqqipRO28ndG/91tLSwqFDh5g/fz6enp7WNm1MUllZyblz56ivr0cmk0kLhv7+/tY2rV+0Wi3nz5+nsrKS1tZW5HK52dKeBf/FxcVFKu+Qy+V4eHggl8vNdvycnBy0Wi1ubm4EBweb7bimEh4eTm1tLQUFBVZzus9UD90ezdQlpe6tSg1Y4nMUCARDY7LTffvtt/POO+/w2muv9dj+7rvvcuutt5rNMIFgLLB48eJ+/xaYl/PnzxMeHo5CoZC0JrqjVCopKioChGCdvdC99ZunpyfLli2ztkljloKCAo4ePUpISAgxMTEA1NTU8NNPPzFnzhzCw8OtbGFfjh07RkVFBSEhIfj4+IiFSwsxbdo00tLSmDVrlkXm4dmzZwGIjY216mcYHh7OqVOnxlxdd/dWpQbE9VQgsA7DFlLbuXOnlJpy5MgRCgsLueOOO3jiiSek/Xo75gKBQGAJuqfH2muqrKAnvVu/CSzHmTNnmDp1quRwGzh37hxnzpyxSae7rKyMxMRE/Pz8rG3KmObIkSNotVq2bduGQqHoEx1duXLliI7f3em2Joa67tLS0h4lLfZO91alnp6efRY2xCK0QDB6mHxVSU9PZ+bMmUBXWhCAn58ffn5+pKenS/uJVWeB4L/qwL3p7OyU+p0aUvcuueSS0TRtTKLT6di+fTuJiYl4eHgAXWn9e/bsYfHixTYtDCXoSffWb0qlkvT0dBISEiQnXIhlmY+WlhYptbf7fAkODub06dNWtq5/nJ2dJcdIzHHLMWPGDOnvlpYWMjMziYuLM1srOVtxur29vXF3d6epqYmSkhKbXGgaDt1blRqE07RaLe3t7Tg7OwunWyAYRUx2uvfs2WMJOwSCMUnvfrElJSU4ODjg5uaGXq+nsbERrVYrHAgzIZfLpRr67gi9Cfujv9ZvqampAKL1m5lxcXGhsrJS6oFumC8VFRW4uLhY07QB6Z72DGKOW4rugoV1dXVkZGQQHBxstsUNW3G6oSvFPD09nYKCgjHjdPeX+WUQUluwYIEVLBIILlxMdroN0bn+OH36NAkJCSM2SiAYK1x00UXS36mpqYSEhDBr1iwaGhqkH73c3Nw+zrlg+ERFRXH27Flmz54thGLsmO6t30TbN8syadIkTp06RX19vdRN4cyZM5SVlfWIdNoS3t7eUtqzYZ7v2bNH+nukac+CvhhaTWk0Gjo7O4G+C8umoNfrbcrpDgsLk5xugUAgMDcmO90JCQm8//77fVbPXnnlFX73u9/R1tZmNuMEgrFEfn4+ixcv7uEIymQyJk2axI8//si0adOsaN3Yoa6ujoqKCsrLy/H09ESv1wNdix4ODg4ijV8g6EV0dDROTk5kZWVRWFgIdKUSz58/32azcA4fPkxbWxtTpkxBp9ORkZFBTEyM2dKeBV1oNBrS0tIoKiqio6MD6FrcMJQQjiTjpLy8nMbGRuRyuU206TJEt4uLi/vNmLJXWltbKS0tpbW1FZ1O16NlqWgbJhCMHiY73U888QQ33HADd999N6+99hq1tbXccccdnD59ms8//9wSNgoEYwKdTkdTU5NUa2ygqalJcgwFI8fBwYGQkBDpuVqtBroUzEVGgW0zUOs3w2JuYWGhVJcoahHNg06nIzMzk8jISC677DIpq2DOnDk2nVVQU1PDkiVL8PLyskjas6CL1NRUqqqqmDVrFocPH0an0xEVFUVpaSlTp04d0bENUe4JEyagUqnMYe6I8Pf3x9nZmba2NsrLywkKCrK2SSOmoqKC/fv34+bmRmNjI56enjQ3NwNd9x4CgWD0MNnpfvLJJ1m6dCm33347U6dOpba2lrlz55KWlkZgYKAlbBQIxgSRkZEcO3aM5uZmPD09mT17NuXl5WRlZREZGWlt88YM3VP6oStS09TUhLu7+5hRpB2rDNT6Ta/X4+TkRFFRkRRhE063eZDL5Zw7d06q3XV3d2fp0qVSfbet4u7uLqU724vN9khZWRkXXXQRAQEByOVy5s+fz7hx4/D19R1x7bMtpZZDV+ZZWFgY586do6CgwGZLK0zh9OnTxMTEMGXKFDZt2sTFF1+MUqnk0KFDNpvFIhCMVYZ1BxodHc2UKVP45ptvAFi7dq1wuAWCIZg2bZqUwmmI3Dk7OxMTEyMcCAvQ3t4ureQLh9s+EK3frENAQABVVVW4urqiVCrtIlo8depUUlJSSEhIwNPTUxKnNEetseC/qNVqKWXfwcEBJycnlEolfn5+nDx5ckTHtjWnG5CcbkOZhb3T2NgopZDLZDK0Wi1ubm7MmDGD/fv3i3sPgWAUMfku9MCBA9x22234+PiQlpbGgQMHeOyxx9i2bRv/+Mc/7OLHWiCwBjKZjNjYWGJjY6mvr+f8+fNMnjxZ1CCaGY1Gw8mTJykoKOiRth8SEsJFF10knG87wVCjq1QqaWlp4ezZs8TGxqJSqTh37hzx8fHWNnHMEBgYSFpaGvX19bi4uFBeXk5ISIiU8muLEbF9+/YB8NNPP/UpzxHq9ubD1dWVlpYWXF1dcXFx4fjx4yxYsIDS0tIRL2zYotNtiNwXFBSg0+nsXoxTqVRK9enOzs40NzejVCo5c+aMVKMvEAhGB5PvPi+77DJ++ctf8n//9384ODgQFxfH4sWLue2220hISKC4uNgSdgoEYwq9Xk9eXh7R0dHC6TYzKSkpVFVVsWDBAvz8/Kivr2fPnj3U1NSQmpoqtRgS2DZnzpwhKioKpVKJWq0mJyeHCRMmSDeMF6rTrdVqSU5OpqysjKCgIBITE1EoFCM6piFi2b2Ovry8HLBdB3bRokXS301NTZw4cYJZs2aJFHMzExERQX19PQEBAYSHh3Py5Em2b9+OXq8fsfinLTrdgYGBODg40NbWRmZmpt1fZ3x8fKiqqsLDw4PAwEBSU1MZN24cxcXF+Pj4WNs8geCCwmSne+fOnVx66aU9tkVFRXHgwAFeeOEFsxkmEIxFioqKKCoqktKeDx8+LN0wX3HFFdY0bcxQXFzMxRdfTEBAAIAU2Z48eTLp6enC6bYTBhIXrK+vx9HRcZStsQ02bdrEunXreixuh4SE8MYbb7Bq1aphH9ce27MZ5jf8N5Xcx8fHpm22R2JiYqS/fX19AZgyZQqBgYF4eXkN+7gtLS1SCrctOd0KhYKQkBDy8vLYt2+f3Tvd06dPl3rYT5kyBY1GQ0VFBdD1mygQCEYPk53u3g63Ablczu9+97sRGyQQjFWysrJIT08nIiKCkpISoOtmsampySbapYwVtFqt1Gu4O46OjtLNh8B22bx5MzKZDJlMxvbt25HJZFJ65J49e9BqtUyYMMHKVo4+mzZtYvXq1X0WI0pKSli9ejUbN24cluOt0+n45ptvuOKKK/D09DSXuRahvr6+3+2GRcympiZJaG8kDqGgC51Ox759+/pkEIwbN27E42vIqvDz85OceVshPDycvLw8kpOT+Z//+R9rmzMi3NzcpL+VSiWzZ8+WFtacnZ2taJlAcOFhtNN91VVX8cUXX0g/yn/+85956KGHpAtvTU0NiYmJnDlzxiKGCgT2Tk5ODrNnzyYsLIy8vDy0Wi2zZs2ipKREamslGDm+vr6kp6czd+7cHmm3ubm5NndzJ+jLjBkz0Ov1HDt2jPj4eBwdHWlpaSEjI4PY2FgCAgLw8/OztpmjilarZd26df1G//V6PTKZjMcff5wVK1aYnGoul8txcXGxi7aFO3fuRCaTDWjr4cOHAdtNibc35HI5DQ0NFjm2LaaWGzDUde/bt0+aX/aOVqulo6MDvV4vCbm2tbWJzBCBYBQx2unesWNHD9GFP/3pT6xZs0ZyujUaDefOnTO7gQLBWKG1tVVy+gwpbCqVivDwcHbv3s3MmTOtbOHYYPr06SQnJ/P999/j5eWFVquVbh4HytQR2A6G1lWurq74+fkhl8tpbW2ls7OTiIgIXFxcrGugFUhOTh5UL0Wv11NUVERycnKPWmdjiYuL4/Tp01x00UWoVComTZpkE32TezOQon1bWxt5eXlERkaK6J2ZMSwST5061azfDVt2usePH49cLqekpIT8/PxRb+mp1+koKCykubkZNzc3wsPCkA1T0K2pqYljx45RU1Pz3+P/vGi1f//+HqUlAoHAshjtdPdeWbaHVXGBwJZwcnKS2q8YHAqDUrDAfHh5ebF8+XIKCgqktNPIyEjCwsKEcrkd0b1mV6VSSbWVF2JLqLKyMrPu15vs7Gyam5v5/vvvcXV1RaFQUFlZKb1uK3oTA4lOGq6nAvOj1+vJycmhoqICb29vlEplD8G96dOnD+u4tux0Ozg4EBwcDHQteI2m0302M5PtSUk0NjZK2zw8PFi+bBmxcXEmH+/o0aPI5XIWLFiAk5PTmIjaCwT2irgDFQhGiYCAAEpLS/H29iYsLIxTp05RWFhIfX09ISEh1jZvzFBVVYWvry9RUVFAl5PW0NCAVqulrq4Of39/K1soMAaNRkNaWhpFRUU9sqwMN40XUvpwUFCQWffrTfeWYIY0VJVKNWJV9NGgoaGBpqYmmpqapAUDsM02Z/ZIQ0ODlNHY2NiIRqNBqVRKugvDxZadbuiZYn7HHXeMyjnPZmayYcMGeoe0mhob2bBhA2vWrOGSSeEmHbO+vp6lS5fi4eEhbTP8Jtq6hoNAMNYw2unu7wIrVswEAuOZPXu2lCESEBCATqfDycmJKVOmXJDCUJZi7969XHvttZKYWnNzMz/++COXXnop+/btu6CcNXsmNTWVqqoqZs2axeHDh9HpdERFRVFaWsrUqVOtbd6okpiYSEhIyIAp5jKZjJCQEBITE4d1/O4KzfaiXt7c3MyBAwf61BxfiIsylmTx4sXS3+b6bmi1WilabutOd3Jy8qicT6vVsj0pqY/DDaAHZEBSUhIXT3zApHtvDw+PPv24Db+Jtj7HBYKxhknp5XfddZdUy9Pe3s5DDz0kpXv1ntQCgeC/6HQ6MjMziYyM7FGTGhsbK370zMxApS+dnZ12EbkTdFFWVsZFF11EQECAdJM5YcIEfH19KSgokG6KLwQUCgWvv/46q1ev7vOaYWxef/31C+r7ferUKVxdXVm0aBFbt25Fq9Uye/ZscnNzR9w/WvBfCgoKGD9+vFlLcwoLC2lvb+fmdU/S7u7HmeqmAfe1lvRlaGgoMpmMrKwsKioqGDdunEXPl5yc3COlvDd6oKGxkcLCwiGvfYYSHICpU6eSlpZGQkICnp6eyOVyqYuH6OYhEIwuRl9F77zzzh7Pb7vttj77jFYKjkBgb8jlcs6dOyeJRAnMz4EDB4AuJ+TYsWPIfxaeMdyAnDx5UtR92hEG/QPoanWj1WqBrhZDJ0+etKZpVmGgVFBfX1/++c9/jqhPt06nIysri+LiYpqbm4Gu9myGObRy5cphH9tS1NTUsGjRIlQqlbTw4O3tTUJCAqdOnbKZOnR7JyUlhRMnThAcHIyPj49ZjmlILff19R22QJilMWShnT59muTk5H4XvMyJsXoMBp2SwTC0XTSg1+v56aefejyHrjkuhNQEgtHDaKf7ww8/tKQdAsGYJyAggKqqqgHFgAQjwyCspdfrUSqVUtTPcIMREhLSI41WYNu4urrS0tIiCQ8asqlKS0svKBE1A3/84x8BePTRR7nhhht46aWXSEpK4vLLLx+Rww1w5swZcnNziYmJ4fTp00DX9aq6uprJkyeP2HZLYJjn0DX3DVE7V1dXoxwTgXFce+21lJeXU1hYKH03MjMzmTRp0rAXMQ1Ot60vgi5cuHDUnG5j9Ri690sfiKE6GDQ1NXHixAlmzZpl1DkFAoF5EEJqAsEoERgYSFpaGvX19ahUKpRKJVVVVbS2tgJC+GekXHTRRUDXTXdMTIx0Q15fX09NTQ0TJkywyTZIgv6JiIigvr6egIAAIiMjqa2tZffu3ej1+gsufTg5OZmffvoJBwcHfvOb3xASEoKTkxNJSUls3bqVtra2EbXKKigoYPbs2QQHB5Oeno6joyNTpkyhsrKS2tpaM/4n5sPT05OGhgbc3Nzw9PSkvb2d+vp6KisrxcKmGZHL5QQHBxMcHExNTQ0//fQT7e3t7N27F2dn5wHbuA2GvTjdiYmJvP322+zbt29UzvXZD/sGTTH39PAgLCxsyGN17/zQH46OjqhUKqmFqUAgGB2E0y0QjBKGlNju7VZSUlKArpRoIfxjHnpHs728vFixYoWVrBEMl5iYGOnvqKgoAgMDqaurw83NTVJTvlB44YUXALj77rulTgdz584lLCyMwsJCtm3bxg033DDs47e3t0vp6w4ODiQmJuLl5YVSqSQjI2Pk/4AFmDx5shTdnjFjBvv37+fYsWOoVCrmzZtnZevGJr6+vlx99dUUFhaiVquHnVFgT043dIk6WlrtW6FQMHPmTPbu3TvgPpdccsmwBIzVajV5eXmSQ+/h4cHy5ctxdHQcrrkCgWAYCKdbIBglRO2U5di5cyeLFi3C0dGRnTt3DrqvqPW0fXQ6Hfv27WPWrFlSOqUhzfxC49ixY+zYsQOFQsFvfvMbabtMJmPNmjW88sorbNiwYUROt4uLC+3t7bi6uuLm5kZ5eTne3t7U1tZKdd22RmBgoPS3u7s7y5cvp6OjA0dHR9FZxcxoNBpKSkooKCigsrISFxcXQkNDhy1maC9Od3BwMFFRUeTk5HDgwAGuuuoqi52rublZWoR3dHBA3U0MTaFQoNVqOXrsGIunTDQpY6u2tpZ9+/ahUCikmvysrCwyMzO59NJLhZCrQDCKCKdbILAwGo2GyspKgoODAUhLS6OtrY3S0lKCg4MlwZYLSXnY3IwfP562tjYcHR37pOm3t7dTVFREaGio1EZMYNvI5fIeraAaGho4cOAAl1xyyQXXW9YQ5b711lv7tBZcu3Ytr7zyClu3bpXq34fD+PHjqaiowNfXl+DgYNLS0sjJyaG9vZ1JkyaN+H8wJ0ePHu2zrbOzU1KYNtT7G8pNBCPj0KFDlJWVoVAoCAgIQKVSSfOwd7s2Y6itraWyshLALtKbExMTycnJITk52aJO9/r166lHhZenJw8+9BDl5eU0Nzfj5uaGr48v773/HtXV1XzzzTfcfPPNRi8spaSkEBwczOzZs6UFtLq6Ovbs2cPx48dZunSpxf4ngUDQE6OWsGfOnEldXR0Azz//vFSDKhAIhiY/P5+cnBzpeXZ2NvX19XR2dtLY2EhBQQHZ2dlWtND+iY+PZ8eOHezatQsnJycmTZpEfHw88fHxTJgwAbVazYQJE4SQmh0RFhZGXl4e0BX5bm5uRqfTWdmq0SUtLY3vvvsOmUzG+vXr+7w+a9YsJkyYQGtrK1u3bh32eaZOnSoJphlaI4WEhHDxxRfbXE/0/Px8qqqq6OzslB5qtRqNRoNarZa2CcyDTCZj/vz5XHvttcTGxtLW1kZhYSG7du0aMquoP86dOwd0fb/sIb154cKFgGX7de/bt4+//e1vAFx73XU4OTkRERHBlClTiIiIwN3DnZtuugmlUsn58+f58ccfjT52XV0dsbGxfTJWNBrNsBZNBALB8DHK6c7MzKSlpQWAP/zhD1JLEYFAMDSFhYVERUX12DZlyhQAZs+ezbRp0yguLraGaWOKxYsX4+HhQWpqKt9//z1Hjx6lqqrK2mYJholerycnJ4cffviBM2fOAF037CkpKVIa5ljnT3/6EwA33ngjsbGxfV6XyWSsXbsWgK+++mrY5zEow0NXZohhmy2qxEdFRdHZ2UlLSwv+/v7Mnj2b6dOnAzB9+nQuueQSLrnkEusaOYaYN28eQUFB1NTUkJ6eDnQJ7wUEBLBkyRKTj2dILe/v+2yLGOq6jx49Sltbm9mP39rayj333AN0Bbh6Z7MYCAoKkrRJ9u/fLynJD4VSqRwwUGbO3usCgWBojJpx06dP5+6772bBggXo9XpeeeUV3Nzc+t332WefNauBAoG909zc3CMlVqFQ9EgN8/HxGVSxVGAc/v7++Pv7M3PmTIqKisjPz2fPnj24uLgAPR0Lge3S3NyMq6srDQ0NkmCa4aaxqamJtra2C6Jm99y5c2zYsAGAp59+esD91q5dy4svvsi2bdtobGzEw8PD6HPU19ezf/9+2tracHNzY968eRw5cgSAkpISCgsLufjii22qs8KsWbOYPn06JSUl5OXlcfr0aak22NAeUGAe2trayM/PJy8vj87OTkkVe9q0aYSGhg7rmPbmdEdFRREUFERZWRlHjx7l0ksvNevxn3nmGXJycggJCeGKIVK9p0yZgrYskeTkZLZs2SKVgwxGaGgox44dY9q0adI8KS8vB3rqIggEAstjlNP90Ucf8dxzz7F161ZkMhnbt2/vd4VMJpMJp1sg6EVnZydarVZ6vmLFCqlcA7puFLu/LhgZSqWSyMhIIiMjaWpq4uzZs+Tl5ZGcnExQUBALFiywtomCQdi+fTvXXnstixcvBrpqSiMjI9m3bx+zZ8++YIR/XnzxRfR6PStWrBg0xXvq1KnExMRw7tw5tmzZwm233Wb0OdLS0vD09GTevHnk5+ezf/9+/Pz8KC0tZdGiReTl5ZGZmWlTTjd0LVyGhYURFhZGS0sLmZmZQFcEcPny5TYZobc3kpOTqa6uJjAwkOnTpxMYGEhDQwNFRUUjOq69Od0ymYzExEQ2bNjAvn37zOp0Hzp0iNdffx2Af/7zn6iM0BxZvHgxFRUVZGVl8eWXX/LAAw8MGASDrgUSmUzG0aNH0ev16PV6KdV84sSJZvk/BAKBcRjldMfExPDll18CXQI3u3fvHrIPoEAg6MLZ2blPBMrNzY2FCxdKSsGGaKzAvLi7uzNlyhSUSiX5+fmUlZVZ2yTBEPSOVpaVlREXFyfNlwuBvLw8Pv30U2DwKDf8N8X8+eef56uvvjLJ6a6trWXRokV4eXnh6elJbm4uc+fOJTo6Gnd3dyZOnMju3btH9L9YGplMhkqlEiKJZqa8vJyJEycSFRUldRBwc3NDJpON6PfK3pxuQHK6zVnX3d7ezj333INer+eOO+7gqquu4kz10C3YZDIZq1at4v3336eqqoqvvvqKO++8c8BUcYVCwYwZM0hISJBKQ1UqFQ0NDSZlxQgEgpFjckHHhSZkIxCMlKCgINLT0wkKCpIUyh0cHAgMDESj0ZCRkUFQUJCVrRx7VFVVkZeXR3FxMTKZjJCQkAHr5QS2jVKpvKBSIV966SW0Wi1XXnklc+bMGfJm/LKb7uT5559nx44d1NfXG93HXK1WS86qg4MDSqUSFxcXaXFDq9VKvbBtCa1WK6WXV1dXExQUxJw5cwgMDLwgSg9Gg8suu4zc3Fx++OEHPDw8CA8PJywsDBh+LbBarZZERWNjY7EXGS+DmNrBgwfRaDRmqYX+/e9/z9mzZwkMDOSvf/2rSe9VqVTcdNNNvPfeexQXF7N161ZWrlzZY58DBw4MeZzc3FycnJwIDAwU3VMEglFgWFeOnJwcXn/9dSmla/Lkyaxbt66PWJRAIIC4uDiKiorYvn27FEFSq9UUFBRItdxxcXFWtnJs0NbWRl5eHvn5+TQ3N+Pn50d8fDwdHR1MnDgRZ2dna5soGIL+nKaOjg7y8/OJiooa859hcXExH374IdBV72kM/v7+xMfHk5GRwbfffstdd9017PN3H2tbdGBPnDhBUVERzs7OREZGMm/ePHQ6HTk5OXh5eY3578do4evri6+vLzNmzKCwsJD8/HxSUlLQ6/UUFxfj4uJichp/Tk4OWq0WNzc3goODaaixD1HeKVOm4OXlRX19PadOnWLOnDkjOl5paSl/+ctfAPjHP/4h9c82BR8fH2688UY+/fRTUlNTCQwMZN68edLrg302Wq2WxsZG3N3daW5u5tChQ8TExODv72/6PyMQCIzGZKd7x44dXHfddZJKKHStqMXHx/P999+Lnn8CQS+cnJy47LLLOHnyJKdPn+6RPuvr68vcuXNFaqQZ2LdvHxUVFahUKsLDw4mMjMTDw4O6ujp++OEHQkNDxQ25HaDX6zl27JhUd6jVaklJSaGmpobq6mrpZnKsKlT/5S9/Qa1Wc+mll5qkP7B27VqeffZZvvrqK5Oc7sHG2hajXzk5OVI0vqqqSmofVllZeUF8P0YbpVLJhAkTmDBhAsXFxRw8eJDz589z9uxZxo0bZ9J3tHtquS0u6AyEXC5nwYIFbN26leTk5BE53Vqtlu+++w6dTsfNN98sKZIPh8jISK688kq2b9/Ozp078ff3l4Jfg/WpN/wmXnTRRXh7e1NaWkpubq5wugUCC2NUy7Du/Pa3v+WXv/wlR44c4bXXXuO1117jyJEjPP744/zmN7+xhI0Cgd1jqOG+7rrrWLJkifSDOHPmzAumTtXSyOVyLr74Yq655hqmTZsm6tXslIiICFQqFQ4ODjg4OBAeHo5KpQK6HADD9rFIRUUF7777LmB8lNuAoXXYrl27qKmpMeo9Q421YQHLloiIiCAgIECy2ZAWD2P/+2FtXF1dga506+5RVWOxx3puA4bWYfv27RvRcfbt20dlZSUBAQG8+eabI7broosuYubMmej1ejZu3Gj03O+On5+fWPgXCEYBkyPdmZmZUhuT7txzzz2SCqNAIOgflUqFSqWSIksC8yFUyccG/UVo6urqKC0tJT4+fkyrl7/22mu0t7czd+5ck3sgT5o0ienTp5OSksKmTZu4//77h3yPPY61Pdo81pDJZIwfP95kVXt7droNdd379+9Hp9MN6ze8vLyc/fv3A/D2229LLbxGylVXXUVVVRVFRUV8+eWXPPHEE0brOgA4OjoOuwWcQCAwHpOvGv7+/qSkpPTZnpKSIhTNBQKBQCAYBjU1Nfz9738HuqLcw0m/NUS7v/rqK7PaJhCYA3t2umfOnImzszM1NTWSnpEpdE8rnzx5MqtXrzabbQqFgrVr1+Lh4UF1dTU333yzaEMqENggJjvd999/Pw888AAvvfQSycnJJCcn8+c//5kHH3zQqJV1gUDQJXISFhYm0iBHATHW9s+F8Bm++eabNDc3M23aNK6++uphHWPNmjUA7Nmzh4qKimEdwx7H2h5ttkdGMs56vd6unW5HR0cppX44rcMOHDggtQe96qqrzG0erq6u3HzzzTg4OJCUlMT69esH3FfMF4HAOpicXv673/0Od3d3Xn31VWlSBwcH8/vf/55f/OIXZjdQIBiLuLm5DasmTmA6Yqztn7H+GTY0NEj1ncONcgNMmDCBOXPmcOzYMb755hsefvhhk49hj2NtjzbbIyMZ5/LychobG5HL5URHR5vZstFh4cKF+MfPpFruPGgbP99ezysrK6Va8OXLl0u18eYmMDCQFStW8Oabb/KXv/yFhIQEbr/99j77ifkiEFgHkyPdMpmMX/7ylxQXF9PQ0EBDQwPFxcWsW7fOrtQoBQJrotVqaWpqEilgo4AYa/tnrH+Gf//736mvrycuLo5Vq1aN6FiGaHd/2ivGYI9jbY822yMjGWdDlHvChAmSWN9ooNfrqa+vp7Kykvr6+h7dQ0zFIKZWUFAARh5Hp9Px3XffodVqiY2NZcqUKcM+vzHEx8fz9NNPA12ZqUePHu2zj5gvAoF1GJGak7u7O+7u7uayRSC4YGhsbGT79u1Sn26B5RBjbf+M5c+wpaWF1157DYCnnnpqxCKLBqd73759lJaWmvx+exxre7TZHhnJOFsjtby6upojR46QmppKZmYmqampHDlyhOrq6mEdb968ecjlchobG6lvaDDqPYcOHaK0tBQnJ6dhl42YyvPPP8+KFSvo6Ohg5cqVfa4DYr4IBNZBSCgLBAKBQGAl3n33Xaqrq4mKiuKmm24a8fHCwsKYP3++1EJIILAFRtvprq6uJiMjg46Ojh7bOzo6yMjIGJbj7erqSnBQEACFBQVG2bBnzx4Ali1bNmrtQeVyOZ988gnx8fGUlZWxcuVK2traRuXcAoFgYITTLRAIBAKBFWhvb+cvf/kLAOvXr5f6TY8UoWIusDVG0+nW6/VkZ2cPuk92dvawUs3Dfu5bX1BYOKQNhrTyiRMnMm3aNJPPNRLc3d357rvv8PHx4dixYzzwwAMjSq0XCAQjRzjdAoFAIBBYgQ8//JCysjJCQ0P7FTwaLjfeeCMymYyDBw9SVFRktuMKBMNlNJ3uhoaGPhHu3nR0dNBgZIp4d8J/drqHinQfPnyY4uJiVCoV11xzjcnnMQdRUVF8/fXXKBQKPv30U1599VW0Wi3Hjx8H4Pjx46KuWyAYRUxyujs7O1myZAnnz5+3lD0CgUAgEIx5Ojs7+fOf/wzAb37zGxwdHc127ODgYEn0abiCagKBuWhpaaHw58jwaDjdarXarPt1JzQ0FIDqmhpampv73aempoYff/wRgCuvvBIPDw+Tz2MuLrvsMl5//XUAfv3rXxMYGMiDDz4IwIMPPkhERASbNm2ymn0CwYWESU63g4MDaWlplrJFILhg8Pb2Zs2aNXh7e1vblDGPGGv7Zyx+hp988gmFhYUEBgZyzz33mP34w00xt8extkeb7ZHhjnNWVhYAfn5++Pr2bqhlfoxdwBrOQpezszPjAgIAKOwni0Sv17NlyxY0Gg1RUVHMmDHD5HOYm0ceeYTLL78c6Kozz8vLY+3ateTl5VFSUsLq1aulTASBQGA5TC4gu+2223j//felFXqBQCAQCATGo9FoePHFFwH43//9X5ydnc1+jhtuuIHHHnuMY8eOkZeXR2RkpNnPIRAYw2iLqHl6eqJUKtFoNAPuo1Kp8PT0HNbxw8LDqaispKCggLi4uB6vHTt2jMLCQhwdHbn22muHdXxzo9PpyMzM7Pc1vV6PTCYjKSmJmJgY0fpXYDckJydz9uxZqqurUSqVhIaGcvnll+Pn5yfto9Fo2LFjBxkZGWg0GqKjo7nqqqtGTdSwNybXdGs0Gt555x1mz57Ngw8+yBNPPNHjIRAIhqaxsZHdu3eLlh2jgBhr+2esfYYbNmwgOzsbX19fKdXT3IwbN45FixZJ5zMWexxre7TZHhnuOI+2093U2IR2EIcbIDo6etgOZnhYGNC3rruuro5du3YBsHTp0mE79eYmOTmZkpIS6XlQUBD/93//R9DPSux6vZ6GhgapBEAgsAcKCgqYM2cO9957L7fffjs6nY5PP/20R9lIUlISWVlZ3Hjjjdx11100NTVZteTKZKc7PT2dmTNn4u7uTlZWFqdOnZIeKSkpFjBRIBh7aLVaampqhIjJKCDG2v4ZS5+hTqfjhRdeAOCXv/ylRVfch5Nibo9jbY822yPDHefRdLqbm5s5cyYDPeDh7o5KperxuoODA/Hx8T2iYaYSFtYlplZeXk5HezvQ5bh+v2ULnZ2dREREMGvWrGEf39yUlZX1eO7k5MSkSZNwcnLqsb2pqWk0zRIIRsRtt93G9OnTCQgIIDAwkBUrVtDQ0CB939vb2zl16hRXXnklkZGRBAcHs2LFCoqKiiguLraKzSanlxt6DgoEAoFAIDCNb7/9ljNnzuDp6cmjjz5q0XOtWrWKhx9+mFOnTnH+/HkmTpxo0fMJBP0xmk73kSNH6FCrcXVxYerUqcgViq4obkEBdfX1+Pn5dTnc2tZhn8Pdwx1vb2/q6uooKi4mOjqakydOkJefj4ODA9ddd51NpWkbItpD4e7ubmFLBIKhaWpq6pFNo1Kp+iye9YehY4GhXKusrAydTseECROkffz8/PD09KSoqIiQkBAzWz40w24Zlp2dzY4dO2hrawMQ/f8EAoFAIBgEvV7PH//4RwAee+wxi6ef+vn5SQJKome3wBpotVpJSG00nO7FixcTERHB5Ph4FEolMpkMLy8vQkO7UsJrqqvNcr8a9nOK+fHjx8lIT2fnzp0ALFmyxOYE/RITEwkJCRlwIUAmk+Hp6Sn9TwKBNZk8eTKenp7Sw6B/Mhh6vZ6kpCRCQ0MJ+FnosLm5GYVC0Sejw9XVleYBOg9YGpOd7pqaGpYsWcKkSZO46qqrpDD+vffey69+9SuzGygQCAQCwVhg+/btnDp1CldXV9atWzcq5xyuirlAYA4KCwtpb2/H0dGRiIgIi59PLpcTHh6Oi4tLj+1eXl3iaurOzhHX/p/NzCTr3DkAzp07x8ZvvkHd2Ymfnx8XXXTRiI5tCRQKBW+88QZAH8fb8HzZsmU2FZ0XXLicOXOGhoYG6bF+/foh3/Of//yHyspKVq9ePQoWDh+Tne5f/vKXODg4UFhY2OOitnbtWpKSksxqnEAwVnFxcWHu3Ll9bgwE5keMtf0zFj5DvV7P//3f/wHwP//zPyOqKTWFlStX4uDgQHp6OmfOnBlyf3sca3u02R4ZzjgbUssnTZqEQqGwiF1lZWVs3bp1ULVymVwutSurqqoa9rnOZmayYcMG2n6u5e5OTXU152y09daqVavYuHEj48ePp7KykrfeeovKyko8PDzYuHHjqIncCQRD4e7ujoeHh/QYKrV827ZtnD9/njvvvBMPDw9pu5ubG1qtlvZec7WlpcV+1Mt37tzJSy+91CcXfuLEiRT0UnIUCAT9o1KpCA8PN6pORTAyxFjbP2PhM9yzZw+HDx/GyclpVLPCvL29ueKKKwDjot32ONb2aLM9MpxxtnQ9d2dnJxs2bODEiRPs3r170H0NC1011dXDOpdWq2V7UhKDJacnJSXZbLnlqlWryM/PZ+vWrUyePJmWlhaCgoK4/vrrrW2aQGAyer2ebdu2cfbsWe64444+ZR1BQUHI5XJyc3OlbdXV1TQ0NBAaGjra5gLDcLpbWlr6XeWsra0VP3gCgZG0t7dz/vz5PitwAvMjxtr+GQufoaGW+/777ycwMHBUz21IMd+wYcOQDoE9jrU92myPDGecLel06/V6MjMzqa+vx9vbm4ULFw66v4+3d1d/bi+vYSndJycnD5qargcaGhttuvWWQqFg3rx5PProo/j5+XH27FlSU1OtbZZAYDLbtm0jLS2NVatWoVKpaG5uprm5mc7OTqBLpX/GjBns3LmTvLw8SktL+e677wgJCbGKiBoMw+lOTEzk3//+t/RcJpOh0+l4+eWXWbx4sVmNEwjGKm1tbZw6dUoSIhRYDjHW9o+9f4YHDhxgz549ODg48Otf/3rUz79ixQpUKhVnz57l9OnTg+5rj2NtjzbbI8MZZ0s63fn5+dTV1eHg4MDatWsl1eKBkCsUzJs7l9jY2GGluvduvTUQtt56q62tjczMTFasWAHAZ599ZmWLBALTOX78OB0dHXz88ce8+uqr0iMjI0PaZ9myZUycOJENGzbw0Ucf4ebmJi1CWwOTW4a9/PLLLFmyhOPHj6NWq3nyySfJyMigtraWAwcOWMJGgUAgEAjsFkNf7rvuussqaW0eHh4sX76cb7/9lq+++oqpU6eOug2CCxNLOd1VVVVSRPm6665j3Lhxxr1xBGJhY6311lVXXcX777/PF198wa9+9SshpCawK5577rkh91EqlVx99dVcffXVo2DR0Jgc6Z4yZQpZWVksWLCAFStW0NLSwqpVqzh16hRRUVGWsFEgEAgEArvk+PHjbN++HYVCwW9/+1ur2dFdxdxWa04FY4va2loqKysBiImJMdtxW1tbOfezenhISAhTpkwx7QB6PU1NTVJfX2NJTEzEw8ODgVxTGeDp4WFzrbfOVDf1eOQ1dyIfF0bozPnc8b9Pc8n1N9l0SrxAMFYwOdIN4OnpydNPP21uWwQCgUAgsHu0Wi3JycmUlZXx9ttvA3DLLbcwYcIEq9l0zTXX4OzsTE5ODidPnmTWrFlWs0VwYdDdMTanWrBarUYul+Pu7j6sOXUuK4vOklwiIyNNcpAVCgXLly1jw4YNyKCHoJrBEben1lsKhYLJkydz8uRJ0tLSCA8Pt7ZJAsGYxuRIN0BdXR2vvPIK9957L/feey+vvvoqtbW15rZNIBizKJVKxo0bh1I5rHUvgQmIsbZ/7Okz3LRpExERESxevJhbbrlFKruaPXu2Ve1yc3PjmmuuAQZXMbensTZgjzbbI6aOs6VSy728vJg5cyaTJ08eloPr6ekJDK91WGxcHGvWrMG9W2si6CrhWLNmDbFxcSYfc7SRy+W4urkhl8tJSEgAunojD0dcTiAQGI/Jv1D79u3j2muvxdPTU7qJePPNN3n++ef5/vvvh1SPFAgEXTVfl156qbXNuCAQY23/2MtnuGnTJlavXt1v+vbjjz9OSEgIq1atsoJlXaxZs4avv/6aDRs28NJLL/XrsNjLWHfHHm22R0wdZ3M73VqtVhJAc3JyGvZxfH19qZHJaG5upr293eRjxcbFERMTQ0FhIc3Nzbi5uREeFoZMPqw41qjj+HPrN4Dw8HA83N1pr6/g/Pnzol+3QGBBTL5CPPLII6xdu5a8vDw2bdrEpk2byM3N5aabbuKRRx6xhI0CwZhDp9PR2dmJTqeztiljHjHW9o89fIZarZZ169YNWi/9+OOPWzWadNVVV+Hq6kpBQQFHjx7tdx97GOve2KPN9oip42xOp7u2tpYjR45QU1Mz4mM5ODhI0e7qYfbslsnlREREMGXKFCIiIuzG4YauVms6rRa9Xo9MJmPKz9HuoTobCASCkWHyVSI7O5tf/epXPdotKBQKnnjiCbKzs81qnEAwVmloaGDz5s00NDRY25Qxjxhr+8cePsPk5GSKi4sHfF2v11NUVERycvIoWtUTFxcXrrvuOmDgFHN7GOve2KPN9oip42wup9vQ4qqzs9NspYz+/v7A8J1ue6ajvZ2zZ8/S8XO/dUOKeVZWlsnicgKBwHhMdrpnzpxJZmZmn+2ZmZlMmzbNLEYJBAKBQGAtKioqhnz0xtgevsbuZykMKuYbNmwQkeF+6K303N9DMDRqtZqcnBxgZE63VqslIyMDjUaDh4cH0dHRZrHP19cX6FpIUKvVZjmmvRI4bhz+/v5oNJp+7+8FAoF5MKqmOy0tTfr7F7/4BevWrSM7O5t58+YBcPjwYd5++23+/Oc/W8ZKgUAgEAhsGGN7+Bq7n6VYtmwZHh4elJSUcPDgQRYsWGBVewRjk5ycHLRaLW5ubgQHBw/vIHo9WVlZtLS04OjoSHx8vNmUwVUqFR4eHjQ2NlJTU2P1eWlVZDISEhL48ccfOX36NNOnT7e2RQLBmMQop3v69OnIZLIetWpPPvlkn/1uueUWaRVdIBAIBIILhcTEREJCQgZMMZfJZISEhJCYmDjKlvVEpVKxcuVK/v3vf/PVV18Jp1tgEbqnlg/XUS4uKaG+shKZTMbkyZNxdHQ0p4ldtdgymVTfbSvo9XopAu/o6Iinp6fF25AZnO68vDxJHE4gEJgXo5zuvLw8S9shEAgEAoHdolAoeOqpp3j44Yf7vGa4YX799dd76KFYi7Vr1/Lvf/+bjRs32oxNgrHFSOu5Gxsbyc3JwQeIjo62iGPs7e1t9mOOlOrqarKzs3vUVqtUKqKjo/Hz87PYeb28vAgNDaWoqIj09HQpk1UgEJgPo5xuQ2sBgUBgHjw9PbnuuuvMvnIv6IsYa/vHHj5DvV7Pd999B3TdJHe/aQ4JCeH111+3aruw7lx++eV4e3tTXl7Ovn37WLx4sfSaPYx1b+zRZnvElHEeqdPt5uZGYFAQ3p3Nw09PtzOqq6vJyMjos72jo4OMjAzi4+Px9XYZ8XlUTk5Mionps9iWkJBAUVERp0+fFk63QGABTO7TDVBaWsr+/fuprKzsI8Tyi1/8wiyGCQRjGblcPqI+owLjEWNt/9jDZ7hhwwZ27NiBo6Mjp06doqKigrKyMoKCgkhMTLSpaLKjoyPXX389H3zwAV999VUPp9sexro39mizPWLKOJvidPeXTi2Xy5k0aRI+mpYR2TwU7e3tlJSUoNPpmDhxokXPNRg6nW7IDkDZ2dlMmp0w4lRzmUyGUtn39j8+Pp6kpCRKS0upqamRxOYEAoF5MNnp/uijj3jwwQdxdHTE19e3x+SXyWTC6RYIjKC5uZmUlBSmT58uaqcsjBhr+8fWP8P6+nrWrVsHwNNPP01cXBxxcXFWtmpw1q5dywcffMA333zD3/72N+km3NbHuj/s0WZ7xNhx1uv1Rjvd/aZTOzoSPXEifn5+Fq9l1mq1FBcXI5fLmTBhgtUWxwoLC4ds19XR0UFDQwNeXl4jOpdaraa8vJzAwMAeWQsuLi5ERUVx/vx5Tp8+zaJFi0Z0HkH/9Nf9ojfjxo0bBUsEo43JLcN+97vf8eyzz9LQ0EB+fj55eXnSIzc31xI2CgRjjs7OTkpLS+ns7LS2KWMeMdb2j61/huvXr6eiooKYmBh+85vfWNsco7jsssvw8/OjurqaPXv2SNttfaz7wx5ttkeMHefy8nIaGxuRy+WDtvgypFP3djY71GoyMjJGpYe2q6srzs7O6HQ6s/UAHw5NTca1ojNHezOdVktzUxM6rbbPa1OnTgXg9OnTIz6PQCDoiclOd2trKzfddBNyuclvFQgEAoFgTHHo0CH+8Y9/APDPf/4TlUplZYuMQ6lUcsMNNwDw1VdfWdkawVjCEOWeMGHCgPPB2HTq7l1zLIW/vz/AqDj5A+Hu7m7UfpbWLZg0aRIODg7U1tZSUlJi0XMJBBcaJnvO9957L19//bUlbBEIBAKBwG7o7OzkgQceAODuu+/m0ksvtbJFpmFo8blp0yazRNAEAjCuntuUdGpLY1AFr6mp6aNTNFqEhYUNuWCnVCgs3t7M0dFR+txEtFsgMC8m13S/+OKLXHPNNSQlJZGQkICDg0OP11977TWzGScQCAQCga3y2muvkZ6ejp+fH3/5y1+sbY7JLFy4kHHjxlFRUcGuXbu46qqrrG2SYAxgjNM9munUQ+Hu7i51HKirq7OKgJhcLicqKoozZ84MuI9Gq6WgoICIiAiL2jJ16lROnz5Neno6V1xxhchsFQjMhMkz6cUXX2THjh1UVFRw+vRpTp06JT1SUlIsYKJAMPZwdnZm2rRpODs7W9uUMY8Ya/vHFj/D3Nxc/vCHPwDw6quv2qXSr0KhYPXq1cB/U8xtcayHwh5ttkeMHWdjnG5bSac2YIh2WzPFXPtzjXVv6TiVSkVAQAAABQUF5Ofnj+g8SgcHxgUGouwVNDMwYcIEXFxcaGlpIS8vb0TnEggE/8XkSPerr77KBx98wF133WUBcwSCCwMnJydiYmKsbcYFgRhr+8cWP8OHH36YtrY2Fi9ezO23325tc4bN2rVrefvtt/n2229pb2+3ybEeCnu02R4xdpyNcbrDwsJQpZ8fNMVcpVJZPJ3agEFU0FqaDNqfo9gAkRMm4O7u3qOFmkwmw9PTk+bcTMkBHy5KpXLQRUK5XE58fDzHjh3j9OnTREVFjeh8AoGgC5OdbpVKxSWXXGIJWwSCCwa1Wk1FRQXjxo0btZX8CxUx1vaPrX2GGRkZUk/uf/zjH1JbozPVQ6fMTvYzLsI3WlxyySWMHz+ekpISdu7cybJly2xqrI3B1r4fYxVjxrmlpYXCwkJgcKfboGyekZEx4D7R0dEWbxlmwMvLi3nz5o3KufojNTWV9vZ2HB0cGB8cjLyf1mXBwcF4+bmPuK2ZVqulpbkFVzfXAY81depUjh07RmZmJldffXWfUlIDxlzzfLWtQ+4jWmQJLgRMTi9ft24db731lllOvm/fPq699lqCg4ORyWR8++23PV7X6/U8++yzBAUF4ezszOWXX8758+d77FNbW8utt96Kh4cHXl5e3HvvvTQ3N5vFPoHAUrS0tHDo0CFaWlqsbcqYR4y1/WNLn2F7eztJSUlAV0/uSZMmWdmikSGXy7nxxhuBrhRzWxprY7FHm+0RY8Y5KysL6IocD1Vy4efnR3x8fJ/oskqlIj4+Xkr5HutotVr27dsHQGhYWL8Ot4HuTnJdXR2pqaloNBqTztepVlNcXETnIPXyISEheHt7o1arpc9UIBCMDJOd7qNHj/Lxxx8zYcIErr32WlatWtXjYQotLS1MmzaNt99+u9/XX375Zd58803+8Y9/cOTIEVxdXbnyyitpb2+X9rn11lvJyMjghx9+YOvWrezbt09SkxUIBAKBwJzs3r2b5uZmu+rJPRQGFfPvvvuOQ4cOAXD8+HGpxlQgMBZjUsu74+fnx9y5c5k2bRpxcXFMmzaNuXPnWs3h1uv11NfXj+o509LSaGhowNHRkeCgIKPeo9PpOHfuHPX19aSmplpEcC4hIQEQKuYCgbkw2en28vJi1apVXHrppfj5+eHp6dnjYQrLly/nj3/8I9dff32f1/R6Pa+//jrPPPMMK1asYOrUqfz73/+mtLRUiohnZmaSlJTEe++9x9y5c1mwYAFvvfUWX375JaWlpab+awKBQCAQDEhxcTHHjx8H7Ksn91DMnTsXf39/WlpaePTRRwF48MEHiYiIYNOmTVa2TmBPmOR06/V0qtXIZDK8vLwICAjAy8tr1FLK++PYsWOkpqbS2Ng4auecMmUKV1xxBZGRkYNGubsjl8tJSEjA0dGR5uZmUlJSegSkzGUXwPnz52lrazPrsQWCCxGTa7o//PBDS9jRh7y8PMrLy7n88sulbZ6ensydO5dDhw5x0003cejQIby8vJg9e7a0z+WXX45cLufIkSP9OvPQ1fuxu3iHsa0rBAKBQHBhotPp+P777wGYMWOG3fXkHozNmzdTVVXVZ3tJSQmrV69m48aNJmeyCS5MTHG6m5tbOHHyBF5eXkybOhWs6Gwb8PDwoK2tjerqaiZOnDgq53RwcGD+/PlG1Ud3x9XVlenTp3P69Gna2to4deoUU6dONZtd/v7+BAYGUl5ezpkzZyzeqkwgGOvYbPO98vJyoK+4wrhx46TXysvL+6g4KpVKfHx8pH3648UXX+wRnZ88ebKZrRcIBkehUODl5TViQRTB0FhirLVaLXv37uWLL75g7969dpOGa69228J8OXToEJWVlbi4uLB06VKr2WFutFot69atk56r1Wry8vJQq9Xo9XoAHn/8cZv9rmi1Wk6cOIFer+fEiRM2a+dYwJh5aIrTXVNbA3Tdt9mCww3/bR1WVVUlff8thVarHfE5nJ2dmT59Oq6urqjValJSUiguLh70PTK5HJWTEzIj+m8bnHiRYi4QjByTI92RkZGDpv7k5uaOyKDRYP369TzxxBPS85KSEuF4C0YVDw8PrrjiCmubcUFg7rHetGkT69at63FjExISwhtvvGGT0UBD9ORsZibbk5J6pE1+9sM+li9bxqrEi6xlnlFYe77U1dXx008/AXDllVeOqX7QycnJPb7LJSUl/Pa3v5We6/V6ioqKSE5OZtGiRVawcGDsbS7aO0PNQ61WK4luGeN019bWAuDj42MeA82At7c3crmc9vZ2KioqCAwMtNi5Tpw4wfHjx1m6dOmIouqOjo5SxLuxsZGUlBRCQkIG3F+lUhndBiw+Pp4ffviBgoICCgoKCA8PH7adAsGFjslO9+OPP97jeWdnJ6dOnSIpKYlf//rX5rJLutBVVFQQ1E1YoqKigunTp0v7VFZW9nifRqOhtrZ20AulSqXqUYs3mrU7AoHAftm0aROrV6/uE52w9TTcs5mZbNiwgd4xlabGRjZs2ABVxTZpt62wbds2Ojs7iYyMNGv6pi1QVlZm1v1GC3udi2OZwsLCrrZXjo5DpiK3trZK91625HQrFAp8fHyorq4mMzPTYk53Z2cn+/fvp6mpySzCbUqlkqlTp1JSUsKsWbNGbuDPeHh4EB4eTn5+Pl988UWPBTmBQGAaw2oZ1v3xv//7v3z22Wc8//zznDt3zmyGRUZGEhgYyO7du6VtjY2NHDlyhPnz5wMwf/586uvrOXHihLTPjz/+iE6nY+7cuWazRSAwN3V1dWzcuJG6ujprmzLmMddYG9Jw+0sHtOU0XL1Ox/akpD4ONyBts0W7u2PN+ZKRkUF2djYKhYKrr7561M9vaYJ6qSVHRETw6aef9nGaeu9nTXrPxe422/JctHeGmoeG1PJJkyYNWQqSnZ0NgJurq80JEvr7+wNdYr2W4sSJEzQ1NeHp6cmMGTPMckyFQkFYWBjyn9PGdTodOTk5ffZrb2sj88wZ2o0URzMsNH722WdmsVMguFAxW0338uXL+eabb0x6j0FxMSUlBegST0tJSaGwsBCZTMbjjz/OH//4R7Zs2cLp06e54447CA4OZuXKlQDExcWxbNky7r//fo4ePcqBAwd49NFHuemmmwgODjbXvyYQWASdTmdtEy4YzDHWvdNwe9M9DdeWKCgsHDSbRw82aXdvrDFfuvfkXrhw4ZB9h+2RxMREQkJCpLIxmUyGg4NDj+ehoaEkJiZa08we9J6LvW221bk4FhhsHppSz21wun1scE75+Pggk8moqqqipqbG7MfX6XQcOHAA6Jp/SqXJSadDotfr2bp1K59++imFhYXQa7HYlFryuLg4FAoF6enporZbIBgBZnO6N27caHKK0PHjx5kxY4a0yvfEE08wY8YMnn32WQCefPJJHnvsMR544AHmzJlDc3MzSUlJODk5Scf47LPPiI2NZcmSJVx11VUsWLCAd99911z/lkAgEAD2m4bb3Nxs1H62ZrctYOjJ7efnxyWXXGJtcyyCQqHgjTfeAOij12J4/vrrr9uU6KO9zsWxjrFOt06n+6/TbUOp5QaUSiWTJk3i/vvvt4h9paWlNDc34+npKZVLWgI3NzegK6CVk5sLej16vZ6mn38TmpqbjXK+nZycmDRpEiCi3QLBSDB5eW3GjBk9fpj1ej3l5eVUVVXx97//3aRjLVq0aNAJL5PJeP7553n++ecH3MfHx4fPP//cpPMKBAKBqRibXmtLabh6vZ7SkhKj9rUlu22B7j25r7nmGptyOs3NqlWr2LhxYw8Vc+hKs33nnXdsrjba2O+ql5eXZQ0R9MBYp1uv13PllVeS06TGw8NjNEwzmcDAwD7dc8yBVqulqKgI6MqesdR1RSaTcdlll+Hs7ExKaQ3FxcU0NzfT1tqKVqfD28uLrKwscnJyiI6OxtfbZdDjJSQkAPD555/zpz/9SUphFwgExmOy021I7TYgl8vx9/dn0aJFRqUUCQQCgT1iSMMtKSkZdLHwm2++4aKLLsLFZfCbGEtTWFjIvffei0/stEH3k4HNpQ9bm949uW1JsVev19PQ0IBarcbR0RFPT89BO4oYy6pVq1ixYgU//vijVLO7bNkym3O44b9zcajWSHfddRfPP/889957r0VSeAU9MdbpVigUTJs2DQcT+1IPB0vNl+FSXV2NWq3G29ubadMGvzabg/nz59OWfp5z585Jgm3d50JHRwcZGRn4xkZK7dL6Y+LEiXh4eFBUVMT+/ftZuHChpU0XCMYcMr2lGxHaAcXFxYSGhlJUVDRomwWBwFxoNBpaWlpwdXUVN4MWxpxjPZBiskwm67EtOjqajz76yCopyXq9ng8//JBf/vKXNDY2csvjvyFhyhRJO6P3BV8GrFl8sU06VwZGe75s2rSJXbt24eLiwqOPPtpvi7D+omBnjHAiJvu5m2yP4bjV1dVkZ2fT0dEhvaZSqYiOjmZhbKTJx+0PjUbDgQMHWLp0KQ4ODpSXl+PubrrNlmbTpk3ccMMNADg4ODBu3DgqKirQaDTo9XrpOXQ5gX/+85+57rrrhnS4LPUZjgUGm4e1tbWS5kFTU5OU2jwYxoy1r7Z1yH16z0Vj5kvMEJFd6OpZf/LkScLCwqQySHPYXFtbi0KhkFK2e9s9kmND3/HIqGrk0MGDdGo0QNc1X65QoNNqpd+DYKWWuXPnDjo/1q9fz4cffsiDDz7IP/7xD4vabCyGOW7uY1vquJY+tj1zIfhiIj9EILACSqUST09P4XCPAuYc61WrVnHbbbf12R4SEsI333xDUlIS48ePJzs7m8TERP73f/+XNiMVYs1BSUkJ11xzDffeey+NjY3Mnz+fhx56iOtWrGDNmjW490rllMlkXL9qlU073DC68yU3N9cme3JXV1eTkZHRw4GA/0aqzKW0rFQqWbhwIRMmTKC1tbWrpZwNsmDBAik1t7Ozk+LiYjo7O6W5WFhYyJtvvomvry9nz55l5cqVXHrppRw5csTKltsvg81DQ/eakJCQQR3u5uZmDh48SFVVlcXshKHnS3V19ZDHKCkp6SH2ay58fHz6ONyWpKGhQXK4oWvhVdvN4YaucWloaBj0OLfeeisAX3/9NWq12gKWCgRjG6OdbrlcjkKhGPQhHAiBwDhaWlo4duwYLS0t1jZlzGPusTbcgP3qV7/i888/Z8+ePeTl5bFq1SquvPJK0tPTueuuu9Dr9bz66qvMnDnT4jf6er2eTz75hClTprBt2zZUKhUvv/wyycnJUvQpNi6Ox9et484772TV9dfj4e6OXq83S49YSzNa80Wv1/Pwww/bXE9uvV4vCU8NRFJSklkU3ltaWjh+/Dj3338/AB988MGIj2kJPvroI7RaLXPmzGHXrl188cUX7Nq1S5qLjo6OPPbYY+Tk5LB+/XqcnJxITk5m3rx5rFmzZsjxFPRlsHlobGr5+fPn+eGHH/j2228tYSJg3HzJzs4eUkQsLi4O6CrVMVaQciC0Wi2abo7vaNLbQZbL5bi7ufWpyx7KkV60aBFBQUHU1tZKXR0EAoHxGO10b968mU2bNvX7+PWvf41KpRJOt0BgJGq1mry8PLFaPAqYc6zT0tI4ffo0jo6OPP3009x8880sWrSohxiOl5cXH374IVu2bCEwMJCzZ89y8cUX89RTT/WJupiD8vJyrr/+eu644w7q6+uZM2cOJ0+e5Ne//nUfkR6ZXE5ERAQJU6ey9IorADh44IDN94sfrfmyYcMGduzYYXM9uRsaGob87jQ2Nna1BhohhrG+7rrrUCgUHDx4UHKobAWdTid1KXnooYeYOXMmcrmcmTNn9vnOe3p68qc//Ynz589z9913I5PJ+Prrr4mLi+MXv/iFxSOuY4nB5qEpTjd01QgbMCz+VVZWUl9fb1I7KwMajYby8nLS0tI4e/bskPPFmMiup6en1H7WEMkfLiUlJRw5csQqivqOjo49nsvlcpycnPo43b33641CoeCmm24ChIq5QDAcjHa6V6xY0ecRGxvLRx99xCuvvMKNN9444ouSQCAQ2DKffPIJ0KVm7e3tPei+1157LRkZGdxyyy3odDpefPFFZs+ezcmTJ81ii16v58svvyQ+Pp7vvvsOBwcHXnjhBQ4ePMjkyZOHfH98fDwBAQG0d3Tw2muvmcUme6a+vl5S77a1ntzGLjY0NZlPmMrPz4+rrroKgA8//NBsxzUHP/74Izk5OXh4eLB27Vqj3hMSEsIHH3xASkoKy5cvR6PR8NZbbxEdHc2LL75Ia+vQdaeCgTHG6dZqteTk5AD/dbqrq6s5cuQIqampZGZmkpqaypEjRwZM/9bpdLS0tFBZWUl+fj4ZGRm89dZb/OlPf+Kf//wnmzdvprKy0iibjZlXhmj3SMo3DIrlGo3GKl0QPD09UalUg+6jUqnw9PQc8liGFPMtW7aY9XojEFwIDKumu7S0lPvvv5+EhAQ0Gg0pKSl8/PHHNqXwKhAIBOZEq9VK7Qlvv/12o97j4+PDZ599xjfffIO/vz/p6elcdNFFPPfccyOK2lZVVbFmzRpuvvlmamtrmT59OsePH+epp54yOuNIJpOxePFioKsP84UY8dNqtezdu5cvvviCO+64g4qKCmJiYmyuJ/dQESgD5hY8u+eeewD4+OOP6ezsNOuxR8I///lPoGseurq6mvTeqVOnsm3bNnbt2sWMGTNobGzkqaeeYtKkSXz44Yfof07R1+t05Ofnk56eTn5+vrRd0D/GON2FhYWo1WpcXFwIDg4esu66uLiYqqoq8vPzOXPmDMeOHWP//v0cP36czMxMCgoKqK6upra2Fr1ej5OTE2FhYUYvmBkzrwxOd15eHu3t7UYdtzclJSVoNBpcXFzw9/cf1jFGgkwmIzo6etB9oqOjjVJ1nzlzJjExMbS3t7N582ZzmSgQXBCYlA/e0NDAn/70J9566y2mT5/O7t27RZsZgUBwQbB3715KS0vx9vZm+fLlJr131apVJCYm8vDDD7Nx40aef/55tmzZwscff2xy3fCmTZt46KGHqKqqQqlU8vTTT/P000/j4OBg0nEAYmNiCA4Korm5mZdeeolXXnnFpPfbs9Lzpk2bWLduXZ+2U7feeqvN9eQ2RKoGS5mdPn06Tk5OgyrjmqqIe/XVVxMQEEBFRQVJSUlce+21Jr3fEpSXl0v1wA8++OCwj7NkyRKOHz/OF198wdNPP01BQQH33HMPjz5fRWxMDCmpqTQ2Nkr7e3h4sHzZMmJ/dsIE/0WtVksR7MGc7u6p5cbUXefk5FDfj/K1UqnExcUFV1dXXF1dmT17Nv7+/ri5uSGTycioauTIkSODzhdjI7u+vr74+/tTVVVFVlYWymDTugRoNBqpL3dERITV2pX5+fkRHx9PdnY2Wq1W2q5UKIiJjcXPCDV36HLgb7nlFp577jk+++wzZl91vaVMtkuMUnMfBTsEtonRke6XX36ZCRMmsHXrVr744gsOHjwoHG6BYJioVCpiY2OHTPkSjBxzjfWnn34KwJo1a4Z1LH9/f77++mu+/PJLfHx8SElJYfbs2bzwwgs9BHa6R1/37t0r3SDV1NRwyy23cMMNN1BVVcWUKVM4cuQIv//974flcAMgk7H4sssAePvttyktLR3ecSyMueeLofVbf32en3vuOZurYTZnpGoouo+1g4ODlNVhK4JqH374IRqNhvnz55OQkAAM//shl8u59dZbOXv2LK+88gpeXl5UVlayLzm5h8MN0NTYyIYNGzhrJpV4e2Sgcc7JyUGr1eLm5ibVQPeHwcmOjo6msLDQKI0LFxcXAgMDiYqKIiEhgXnz5nHJJZcwY8YMJk2axPjx45kwYQLu7u7S99+Y+WJs9gh0Rbt9fX2HNb8MUW5XV9dB+2CPBn5+fsydO5fY2NiulmE6HQ4ODviZWEpzyy23ALBr164RC8wJBBcSRjvdv/3tb2lvbyc6OpqPP/6YVT+3men9EAgEQ+Pi4sLUqVNxcTFudVkwfMwx1q2trXzzzTcA/bYMM4W1a9eSkZHBihUr6Ozs5JlnnmH+/PmcOXOGTZs2ERERweLFi7nllltYvHgxERERrF+/nilTpvDFF18gl8t56qmnOH78ODNnzhyRLQDRUVFccskltLe386c//WnEx7ME5pwvWq2WdevWDSrWlJSUNCwxJ0vR0txMeXn5qJyr91jffffdAGzdutWo/rKWRKfT8a9//QvoGeUe6ffDycmJX/3qV2RlZQ3ojBm+DUlJST0ihRcSA41z99TygRzTlpYW6urqkMlkREVFGV0PHB4eTkxMDCEhIfj4+Bi9sGKI7Pbe38HBAZlMRlNTE5mZmUbN84ULF/Loo49KizzGotFopIW98PBwq0W5uyOTyfD19WXixInIgLb2dpqaTHOco6OjmTt3LjqdjjMZGZYxVCAYgxjtdN9xxx2sWbMGHx8fPD09B3wIBIKh6ezspLKy0qbqJMcq5hjr77//nqamJiIiIrj44otHbFNgYCCbN2/mk08+wcvLi+PHjzNt2jRuuOGGPtHX4uJi/vznP1NeXk5sbCyHDh3ihRdeMF+WhEzGH//4RwDeffddCgoKzHNcM2LO+ZKcnNxvhNuAXq+noaHBLErg5sCQhltTUzPofsa0QDKG3mMdHx/P3Llz0Wg0UraHtfjhhx/Iy8vDy8uLNWvWSNvN9f3IyMgYVGtBDzQ0NvL999+P6Dz2ykDjbEw9t6urK08++SR33303zs7ORusPmBKR7o0hsjtt2jTi4uKYNm0a8+fPZ8qUKchkMillfCiGW25SX1+PVqvF1dXVKrXcA6HTamlvb5dq3ysqTV9MMwiqpZ0+bVbbBIKxjNFO90cffcSHH3445EMgEAxNc3Mze/fuFalZo4A5xtrgbNx666192qwMF5lMxm233UZGRoakpjwY7u7uHD9+nIsuusgs5+/OokWLWLJkCZ2dnfzf//2f2Y8/Usw5X4xdVLAVZd6MjAzqh2htBMa1QDKG/sbaIKj2/vvvWzUDwCCgdscdd+Ds7CxtN9f3w9h2Ttdffz3z58/nj3/8IykpKTaVFWFJBhpnY9uFOTg4EBoaCkBYWJjZFLUHQyaT4eXlRUBAAF5eXshkMnx8fJg8eTIymYzy8nKje7Z3dnbSbEJU2M/Pjzlz5jBp0qThmm8R1Go1Bfn5ePv4AFBVWWnyd3jNmjUoFApKSkqoram1hJkCwZjDPHePAoFAMEapqqoiKSkJ+O/qvjkJDg7m17/+9ZD7NTU1cezYMbOf34DB2f7oo48kwaOxREFBAevXr+cXv/iFUfubWwl8OKjVanbu3GnS/pZg7dq1ODs7k5mZyZEjRyxyjqEoLS1ly5YtwMgE1AYjKCjI6H0PHz7M7373O2bMmEFoaCgPPvggW7ZsoaWlZcj3DqTbYK8M5XT359DJ5fJR0ynoDz8/P8nekpIS8vLyBt2/pqaGv/zlL6SmppikZO/s7IyHh8eIbLUUHh4eODs54ePra/J3cNy4cVx++eUAnE4X0W6BwBiE0y0QCASDsGHDBjQaDbNmzZLax5gbY+t1jY3EDYf58+dz9dVXo9Vq+f3vf2+x84wmer2eXbt2cf311zNhwgT+/Oc/09jYOGi6qEwmw9PTk7CwsFG0tC96vX5UeuGeqW7q8chr7kQ+Loy85k5pm6enJ6tXrwasJ6j2/vvvo9VqWbBggVF96IdDYmIiHh4eDOTmyQBPDw/y8/P55z//yXXXXYeLiwslJSW8++67rFixAl9fX5YvX87bb79Nfn5+n2MMpNuwadMmi/xPlkav1w/pdOfk5PD3v/+dAwcO9Nju5+eHVz+RbJVKRXx8vMWFxwICAqQodGFh4aAlJYZ6co1WS11d/aDH7ezstIu+73KZjIsuuoiYmBijW012x7AIffr0abhAsj0EgpEgnG6BQCAYhE8++QQYuYDaYBgbYTMlEjccDNHuL774gvT0dIuey5I0Njbyt7/9jcmTJ7N06VK+/fZbdDodS5YsYfPmzXzxxRfIZLI+UTTD82XLlllN9Ki2tpYff/yRV199lYxuIkXG2FNfX28xuwwp5l9++aVR0VxzotVq+xVQMzcKhYLly5YB9HG8Dc+XLVtGeHg4DzzwAN999x01NTVs376dRx55hIiICDo6OkhKSuLRRx8lMjKS+Ph4fvOb37Bv3z6+/vrrflXzS0pKWL16tV063uXl5TQ2Ng4auT5//jxVVVXU1vZMQ9bpdFKqenR0tFR3PXfu3FFT+g4KCiIqKgro6sVdUlLS734ymUxaVKiqrhr0mMXFxRw/ftwm9TH6MILr3MqVK3FQKqmpqaHUggvCAsFYQTjdAoEVkMlkODs724Sa6VhnJGN9/vx5jhw5gkKh4Oabb7aAdV0kJiYSEhIyoI0ymYzQ0FCLt2mcMWMGq1evRq/X89xzz1n0XKZg7GeYmZnJo48+yvjx43nsscc4e/Ysbm5uPPLII2RkZLBr1y5WrlzJjTfeyMaNGxk/fnyP94eEhLBx48Yha1PNjVqtJiUlhY8++oi33nqL5ORkybH19PRkxowZRmVZlJeXjzzFXCZD6eDQ52Z84cKFTJgwgaamJknJf7RISkqiqKgIHx8fKeLeHXNeT2Pj4lizZg3uvVKCPTw8WLNmTZ8+3U5OTixbtoy//e1v5Obmkp6ezksvvcTChQtRKBScOXOGl19+mUsvvZS1a9f2m2pt2Pb444/bdKp5f+NsiHJPmDCh3xptvV7foz93d2pra9FotagcHRkfHNyj7no0CQkJISIiAugSJBwo88gwB2tqagasge5UqykpKUGv1+Pm5mYRe0dM7zmu19PU1GSyJoK7uzsxMTHAz9FugUAwKKbnkwgEghHj5eXFtddea20zLghGMtafffYZAEuXLmXcuHHmNKsHCoWCN954g9WrVyOTyXrc0BluQF9//fVhq+iawh/+8Ae++eYbNm3axIkTJ5g1a5bFzzkYWq2WlJQUmpubSUlJITExscc4aDQavv/+e/72t7/x448/SttjY2N59NFHuf322/utqVy1ahUrVqwgOTmZsrIygoKCpGOPtDWWQQFdrVbj6OiIp6dnH0dCr9dTWFhISkoKZ86c6eEsR0VFERMTQ3FxMQsXLqRC36XgHB8fT3Z2do/+xo6Ojl3Hbm1Fr9eTlZXFlClThm27k5NTv8JPcrmce+65h2eeeYYPPviAO+64Y9jnMBWDgNpdd92Fk5NTn9fNfT2NjYsjJiaGgsJCmpubcXNzIzwsDNkQIooymYz4+Hji4+N58sknqaurY8eOHWzdunXIUgG9Xk9RURHJycksWrTIbP+LOelvnIdKLa+traWurg65XM6ECRN6vFZd1RUx9vP3H1HE1RyEh4dLLb6ysrJQKBR9rvkRERE4nM6is7OThoYGvLy8+hynqLgYrVaLu7u7pA5ua/Se40XFxTScT5farJlCwtSppGdkkJ6ezhVLlw45RwSCCxnhdAsEAkE/6PV6SbXckqnlBlatWsXGjRtZt25dj/TTkJAQXn/9dVatWmVxGwAmT57Mrbfeyqeffsqzzz7Lf/7zn1E5b39s2rSp3/F44403SExM5L333uOdd96hqKgI6HIMr7vuOh599FEuu+yyISNmCoXC7A5OdXV1H8dYpVIRHR2Nn58fDQ0NpKamkpqa2iPd1sfHh+nTpzNt2jRpkWDOnDkAVFR3OWt+fn74+vr2cei1Wi3FJw/R3NxMTU0N+fn5UuTOnNx555387ne/46effiI7O3tIISxzUFRUJH0HH3jgAYufz4BMLh/xGHp7e3PTTTdx00038dlnnxl1HbGkboMlGMrpNkS5IyIierT/6uzspPrnNngBAQEWttI4oqKi0Gq1lJWVkZmZiYODgxTJha7ri6+fH+Xl5VRXV/dxutU/R7m96XLi7QUfHx8a6IrgazQak+q7o6KicHZ2prm5mbz8/D4LKwKB4L+IJSmBwArU19fz/fffW7QGU9DFcMf6yJEj5OTk4OrqysqVKy1iW29WrVpFfn4+e/bs4fPPP2fPnj3k5eWNmsNt4LnnnkOhULBt2zYOHjw4quc2sGnTJqn+NTQ0lL///e+EhoZSXFzMDTfcQHBwME899RRFRUX4+fmxfv168vLy2Lx5M0uWLLFK6UZ1dTUZGRk9HG7oaueVkZHBiePHef3119mzZw+1tbU4ODgwffp07rrrLh599FESExPRDaGM3F8LJKVSycyZM3FwcAC6lNrPnTs35LH6o729naysLNrb2/u8FhISwpVXXgl0qdyPBu+//z46nY5Fixb1cIC6Yw/X096lDANhad2GkdDfOBvrdPdeoDl//jxarRYnlQoPG+gUYGDixImMGzcOvV7P119/TW5ubo/XDbXm1dXVfcTDioqK0Ol0eHh42GyUG/rOcVdXV9zc3NDr9VRVDV6v3huFQiFFx0WKuUAwOCLSLRBYAb1eT1tb2wXT39WaDHesDVHu66+/HldXV0uY1i+WiL6aSnR0NHfffTfvvfcezzzzTI+07dEgo7KB7SfSufGRJwDw9vLCf/IMrrrzAep+vuHf8LdXmTNnDo8++ihr1qzpN+14NNHpdEP2+21uaSEuLg4vLy8CAwPx8/OTUuUrKytpb2/n2LFjaLVarr/++h6RwaGQyWRMnDiRnJwcOjo6KC8vp62tjfj4eMkZNwq9Hk1n54BqxPfccw9JSUl89NFH/OEPf7BoyYNGo+G9994DBhdQs4frqUG3wVDv2xuZTEZISIjFdRtGQn/jPJjTrVarJTGx3vXcGRkZIHfGPyDA6qnl3ZHJZMTExKDVasnMzOTLL7/k9ttvl/qLe3t7ExkZ2eV8d7Nb3dFBaWkpYAdR7n7meEBAAM3NzVRWVpq88JOQkMDx48fJSE8nIiICT09Po8oxBIILDTEjBAKBoBednZ18+eWXwOikltsiv/vd73B0dGTPnj2j7nQXFBbS2Ng45H4vv/wyd9xxh9UdbuhqOdQ7wt0fBoXmcePG9XFYc3Jy0Ol0dHR0mOYo/4y/vz/z5s1j6tSpKJVKGhoaOHnypFnbF1133XX4+PhQUlLCDz/8YLbj9se2bdsoKSnBz8+P66+/3qLnsjQG3QboX4ler9fz17/+dVR0G8xFS0uL1GarP6e7o6ODhIQEQkNDe0R+1Wo1WVlZQNd31taQyWTExcURFRVFZ2cnn332mZT2L5fLCQsLw8XFpcd7WlpbUcjleHh44OPjYw2zR4Qhxb++vt6o61h3WpqbkctkdGo0fPvtt3z88ce8/sYbnM3MtISpAoHdIpxugUAg6MWOHTuoqalh3LhxLFmyxNrmWIWwsDApuvjMM8+MahTRWBVdW6p/HWk/7draWqqrq5HJZCxfvnxE6fHe3t7MmDEDZ2dn2tvbOXXqVJ92TcNFpVJJC1GW7tltEFC7++67+1XGtjcMug0DpZqbmtprbQyOs0FroDfu7u6sWLGCe+65p8f3OSsrC41Gg7OTE+42qvAtl8tZu3YtYWFhdHR08Omnnw76+Xh7ezN37txR73xgLlQqlVSjXllZafT7zmZm8vXXX6Pr9fvQ1NjIhg0bhOMtsBgFBQV88cUXvPrqq/zhD3+Qsm4M6PV69uzZw6uvvsoLL7zAv//9b2p+1pGwFsLpFggEgl4YenPffPPNJonKjDXWr1+Ps7Mzhw4dYvv27aN2XmNb7dhS/au7kXWp/aWMd09NHz9+/IijfxqNhpqaGkJDQ/H09ESj0ZCens7Ro0dHdFwDhp7d3377bVdtqwUoKCiQvnOjKaBmafrTbXj11VcB+NWvfiU5svbAUPXcA2HoP29rqeW9cXBw4OabbyYoKIjW1lY++eQT2tvagK4FktSUFIqKiqivr0ev16NQKnF2dray1cPHEO2uq6szan+tVsv2pCT6W441bEtKSrLpsg+B/aJWqxk3bhxXXXVVv68fOHCAI0eOcPXVV3Pffffh6OjIp59+ikajGWVL/4twugUCK+Dm5saiRYtst4/nGMLUsW5oaGDLli3AhZtabiAoKIhHH30UGN1od2NDQ4/nTc3N7P3pJ5p+joDLYFT6lptCWFjYkNFYlUqFp6dnn+0lJSW0tbXh6OhoFtXxiooKcnNzKSoqIiEhgcDAQPR6Pdu3b2fbtm2DCqw5OjoS3ktpujfTpk1j5syZdHZ28vnnn4/Y3v5477330Ov1LFmyZEiVdHu7nhp0G26++WYWLVrE448/zpIlS2htbeW2226js7PT2ib2S+9xHszpbmpqorS0tM81o6OjQxJXs8XU8t44OTlx22234e/vT1NTE6lpaZSVlXH27FnqGxrIzc0lNTWVI0eOWGwBytwMNMf9/f2ZOnUqCQkJRh0nOTl50DIgPdDQ2CiVIAgE5mTixIlcdtllxMXF9XlNr9dz5MgRFi5cSGxsLOPGjWPlypU0NTX1iYiPJsLpFgisgIODAwEBAcOq2xSYhqljvWnTJtrb24mLi2PmzJkWts72efLJJ3Fzc+PUqVNs2rTJ4ud788032fztt9JzGV2R26qqKjQaDYa42Gj1LTcWQ63nYERHR/dJG+/o6JDEpiZMmGCW/ykwMBAHBwfa2tqoqakhJiZGauVz7NgxPv/8837VyQHkCgWurq7Ih7DDEO1+//33zb4Y09nZyfvvvw8MLqBmwN6vp3K5nI8++ggvLy+OHTvGH//4R2ub1C+9x3kwpzs1NZV//etffa4Z586dQ6vV4uvri9soClSOBBcXF26//XZ8fHwk5e/eC1eGDgX24HgPNMeVSiXe3t5Gl7YYW94z0tIbgcBU6uvraW5u7tHCzsnJiZCQEKnFqDUQTrdAYAVaW1tJS0szq8CRoH9MHevuvbmt0XbK1vDz8+OXv/wlAM8++yxardYi59Hr9fz+979n3bp1AMydO5c1N96Iu4cHzk5OJEyZgrOTEx4eHqxZs2bU26gZgyEtU97re6NSqYiPj5faDXXHIJrm6enJuHHjzGKHQqGQ6oYNUabQ0FDWrFmDg4MDOTk5fPDBB/2mkXZ2dlJRUTFktPXmm29GpVKRlpbGqVOnzGK3ga1bt1JWVkZAQAArVqwYcv+xcD0NCQnhnXfeAeCFF17g8OHDVraoL73HeTCn21Au0XshypBaHh8fb9Op5b1xd3fn1ltvZSiLs7OzbT6d2pg5bsz/YGx5j7XraAX2RVNTE42NjdLDVGE/+K8uTO/OM66urrS0tJjFzuFw4RYrCgRWpKOjg7NnzxIaGtpHBVVgXkwZ6+LiYvbs2QPALbfcMhrm2QVPPPEEb731FmfOnOHLL7/k1ltvNevxdTod69at429/+xsAixcvZmFiIshkxMbGkpefT3tbG9NnzCAyIsImW9FkZ2dLQmgzZ86kU6NBrVbj6OiIp6fngAs4Hh4ezJkzx+wpxePHj6eoqIiWlhZqamrw9fWV2pV98cUXVFVV8d5777Fg1c09Ut61Gg011dV4enhIEc2Kiop+z/Haa6+Rnp5OWlqaWbNCDAJq99xzj1Ft0/qb4wPZ3B1zLXIYy5nqwSN+Uy+/mltuuYXPP/+c22+/nVOnTtlUynz3cVapVFL9eVRUVI/x1mg0uLq6EhcXR0BAABUVFYwbN462tjbJGZ8yZQr2JRsHjY2N/dYvd6ejo4OGhgZJlMwW6W+Od6egoICysjLi4uL6LYcxkJiYyGc/7KNpiHHZu3cvhYWFLFu2zC5KCgTWZfLkyT2eP/fcc/z+97+3jjFmxvbuXAQCgcBKfPHFF+j1ehITE81SWztW8PLy4sknnwTg97//vVkdxM7OTu644w7J4f7b3/7GwoULpSiYTC4n+OeISnBQkE063BqNRhL9Gj9+PK5ubnh5eREQEICXl9eQGRNyudzs6txKpZLg4GCAHjWVQUFB3HfffZI4VFpqquQw6fV6qW6+qbl5yGjXjBkzAEhLSxswXd1UcnNz2blzJwD333+/WY5pT7z99tuEhoaSnZ3Nr371K2ubMyCFhYW0t7fj6OjYx8Gsq6tDr9fj4uLSo53f2bNn0el0BAQE2KXzZUwbQ+gSeLJn2tvb6ejowbm6uwAAgAtJREFUGFLFXKFQsHzZMoA+GQCynx/xkyejVCrJzc3lH//4Bzt27BhW5FJw4XDmzBkaGhqkx/r1600+hmGxsndUu6WlpU/0ezSxvbsXgUAgsBLdU8sFPXnsscfw9/cnOzubf//732Y5ZmtrK9dffz2fffYZSqWSzz77jEceecQsxx5NDh8+TG1tLY4ODkSEhxv1nvLycsrKyiyaihoSEoJcLqexsZH6+nppu4eHB3fffTdxcXHo9HrOnj1LRkYGR44ckaKXWVlZQ4pDRUZG4unpSXt7O5s3bzaLzf/617/Q6/VcccUVPerxLhS8vLz4+OOPAXj33Xf5/vvvrWxR/xhSyydNmtRnUcnQnq53G7EeqeV2iLHlC7YqhGcsBhXzqqqqIa9PsXFxrFmzBncPjx7bDWVAq2+8kUceeYTY2Fh0Oh2HDx/mrbfeIiUlxebT8EeCXqcjPz+f9PR08vPz0Q8iXinoibu7Ox4eHtJjOAvSXl5euLm5kZubK23r6OiguLiY0NBQc5prEiK9XCAQCOiK1qWlpeHo6MiNN95obXNsDjc3N9avX88TTzzB888/P+KFifr6eq699lr279+Pk5MTGzdu5OqrrzaTtaNHY2Mj+/btA2BCVBQKI1rMqdVqcnJy0Gg0KJVKi0X9HB0dCQoKQqPR9LlxcXBw4MYbb2Tb8dMUFhZKznX3FnkGcSjf2Mh+69FlMhnTp0/np59+4oMPPuDmm28ekb1qtVrq/W2MgJol0ev1NDQ0GFUiYG4WL17ME088wWuvvcZ9993H6dOnJUfIVhisntvgdPv4+EjbWltbpRtge3W6jS0FG6w7gD3g5eWFo6MjarWa2trafnuwdyc2Lo6YmBgKCgtpbm7Gzc2N8LAwKSvJy8uLtWvXkpuby/bt26murua7777jxIkTLF++XMrIGSuczcxke1JSj8wIDw8Pli9bRmw/StuC4WH4fhqoq6ujvLwcZ2dnPD09mTt3LsnJyfj6+uLl5cWePXtwd3c3ucWhORGRboHACjg6OhIZGWlUvaJgZBg71p999hkAV199Nd7e3qNhmt3x0EMPERwcTGFhIf/617+GfZyKigoWL17M/v378fT05IcffhjU4VYoFHh5e9uUWrmBH374gc7OTkJDQxlnpGOUl5eHRqPB3d29X2fWnERHRxMbG9tv/2CZTEZEREQPR1un09He3t7DcRhMHGr69OkA7N69m/z8/BHZ+t1331FZWUlgYCDXXnut0e8z9/W0urqaI0eOkJqaSmZmplVaQr3wwgskJCRQWVnJ/fffbxNRwe7jPJDT3dTUhFqtRqFQ9KgHzszMRK/XExgYOKQTZ6t49IrmDkRubi4ZGRlGp6OPNkNdT2UymbTIM1SKufQeuZyIiAimTJlCxAC6GxMmTOChhx7iiiuuwNHRkeLiYt577z2+//57o89j65zNzGTDhg19PvumxkY2bNjA2cxMK1k29igtLeWf//ynpAGyc+dO/vnPf0q6PJdccgkXXXQR33//Pf/6179Qq9XcdtttPX7vRhsR6RYIrICrqytz5syxthkXBMaMtVarlZxukVo+MM7OzjzzzDM8/PDDvPDCCyTecIvJbZry8/NZunQp2dnZBAQEsGPHDslxGwgHR0ebjIYY0gdlMhlXXXUVtUZEQhsbGykvLwf6byE22jQ0NKDRaKTnOp1Oqus2MJg4lJeXFxMmTECv1/Pxxx/z3HPPDdsWw83Tvffea/T3SqfTUVVVhZOTE1VVVTg7OyMfQd1/dXW1lAbdHUPUPz4+Hvzch318Y3FycuLTTz9lzpw5bNmyhffff5/77rvP4ucdjO7X0oGcbjc3N6ZNm0ZHR0eP77a9p5ZDlxK7Kv38oDXJcrkctF3fo+rqary8vAgJCbGphQZjrqcBAQEUFxdTXV2NVqs124KnQqFg/vz5JCQksGvXLlJTUzl58iT/+7//yx/+8AceeeQRqzpFI0Gr1bI9KalfUTk9XTXuSUlJXDzxAatf98cCERERg/7eyGQyFi9ezOLFi0fRqsGxz2+2QGDnaDQaSdDBXn9g7AVjxvqnn36ipKQELy+vEac426Jqsjm59957efnll8nPz+f4sWPMv/hio9975swZli5dSmlpKeHh4fzwww9MnDhxyPfpdDo61WocHB1H5FDB0ArSAMbcHmu1Wkk8bdasWQQGBlI7xLH1er2k3hwYGGh05MwctLa28t1333HJJZf0iK73Fn2S0dXHV6fV9rh5HEwcyiCo9uGHH/K73/1uWJ9RdnY2u3fvRiaTDSmgZvgMq6uryc7ORt3RIdnsmH6e6OhoYrxN7wqh0+mkz2cwOxdMCh/x93Agul8/xo0bx1dffcXOnTtJT08nMzMTHx8fq10/ul9LB3K6ZTJZn8UZtVotZUHYs9Mtl8uJjo7ud1HGQFxcHKEqKCoqoqKigvr6eurr63F1dSU0NJSAgACrO1zGXE/d3d1xcXGhtbWV6upqs3/n3NzcWLlyJbNnz2bbtm00NDTw+OOP869//Yu33nqrh6Ok1WpJTk6mrKyMoKAgEhMTbTLrKTk5edDsBj3Q0NhIYWEh4UZqfwjGFiK9XCCwAk1NTezYsYOmpqEdAMHIMGasDQJqa9asMbuKtC2h1+upr6+nsrKS+vr6YaWsOjo6SqvL+/fvR22kEu3Ro0dJTEyktLSUyZMnc+DAAaMcbgB1Rwc5OTlGn2s0OHbsGJWVlTg7O3PZZZcZ9Z6ysjKamppQKpWjLhKWl5dHSkoKBw4c6LG9d0q2QqnEx9u7T236YKnbsbGxeHp6UlBQIKX2mcq7774LwLJly4y6ITVEpDs6OnrYbIhIDycVvLCwcEhl5Y6Ojh5q8JZm3rx5REREoFar2bx5s1XrhQ3X0uLiYikdOCYmZsj3VVdXo9frCQ4OtvvSHT8/P+Lj4/v8TqhUKuLj4/Hz88PFxYWYmBjmzp1LaGgoCoWClpYWzp49y5EjRygpKbGqwrmx19Pg4GDGjx9v0bZ1ISEh3H///fzjH//A19eXjIwMLrvsMtasWUNhYSGbNm0iIiKCxYsXc8stt7B48WIiIiLYtGmTxWwaLmVlZUbtJ+77LlyE0y0QCC5o2tra2LhxIzC2U8vNWad62223MWnSJFrb2jhy5MiQ++/atYvLLruM2tpaLrroIvbt28f48eOH82/YBGq1mr179wKwZMmSfuule6PTaqVoX0REhMlp+SPFoNialpZGQ0ODtN3T03PIhSaVSjVov16lUin1tTcIoZlCR0cHH374IWCcgFr3jIGBGKwOfSCMvRkezZtmmUzGypUrcXJyori4mP3794/auQeioKAA6HKYujtkNTU1ZGdn9/h+QZcKNth3lLs7fn5+zJ07l2nTphEXF8e0adOYO3duH30GlUrFhAkTmDdvnlQL39HRQXZ2Nn/961/58ccf+7Q0AvMsjpqD8ePHEx0dbfEWSzKZjAcffJCsrCwefvhh5HI5X3/9NRMnTuSGG26guLi4x/4lJSWsXr3a5hzvoJ9bWw6Fu7vly1MEtolwugUCwQXN999/T1NTE+Hh4VxyySXWNscidI8KdscQFcw0UdxFqVTyhz/8AYCDBw/S3tY24L7ffPMNV199NS0tLVx++eXs3r3bpuobh0NeXh4dHR0EBwdLqdVDIVcoSEhIIDAw0Cr16R4eHkRGRqLT6Th48KC0XSaTER0dPeh7jak9v+eee4Cuz7uurs4k2zZv3kx1dTXjx483qryjoaHBqIh0b+dvKIy9GR7tm2ZPT0+uuuoqoKsU5tixY6N6/t4YFo96p5ZXVlZSUlLS4/NXq9VSu7qx4nTDf9PoAwIC8PLyGnR+KJVKwsLCmDt3LpMmTcLZ2Zn29naSk5N5/fXX+c9//iOpMNuCiJ+18PHx4e233+bkyZMsWLBgwGwAwyLE448/bhMCgwYSExNx7taXvj88PTwICwsbJYsEtoZwugUCwQWNIbX81ltvtVidpjUxJiqYlJRkctrqmjVrCAgIoL2jg4OHDvW7z6mTJ1mzZg1qtZobbriBrVu3WjRV0VSGE1HqLoS2fPlyk74z7u7uxMTEWK2mc8GCBQCcPHmyR4RtoJRZAFcXF6MU1mfNmkVCQgIdHR18+eWXJtllEFC77777jNK4MDY119QU3v6ijr1xdHS0yk1zQkIC8fHx6HQ6brvtNqN7RluCvLw8oKfTrdfr+20VZohyh4aGDpotcSEgl8sJCgpizpw53HjjjQQHB6PRaDh+/Dh/+9vfSDl1atDFUWs53vX19aNaUjFt2jSef/75QffR6/UUFRWNql1D8eabb9LW3j7oPosWL7Z6Tb/Aeoy9O0yBwE4Yiw6erTLQWFdXV0tiWLfeeutomjRqGBMVbPxZ3MUU5HK5JHZz+PBhzp49S3p6Ovn5+eh1Og4eOMCW779Hp9Nx77338tVXX42oXt7cNyrDiSh1X8CYPn06ISEhQ59Irx80E2A0iYyMZPz48Wg0Gg4fPtzjNUPK7KRJk0AmIzIyErlMRktrq1HtfGQymRTtNiXF/Ny5c+zduxe5XG60Onef+nK9vmvBpNeiibEtxNRqNVu2bJHKTAZDp9NJkdvR5uqrr8bDw4OsrCx+/etfW8UGuVzeb6S7sbERjUaDg4NDj0wAw3dn8uTJo2qnLSOTyZg8eTL33Xcfd955J9HR0V194YdoMTackonBbDAGtVpNWloaeXl5PXoiWxrDwuZQ2EJ9tF6v55lnnuGJJ54AYNKkSX1EMg33IBkZGTYVnReMLkI2WSCwAt7e3qxevdraZlwQDDbWGzZsQKPRMHPmTKNuCg2qyXq9noaGBtRqNY6Ojnh6eko3MZZKnB6uKrqx0b7h3LzExsTg7e1NXV0dX331lbRd5ehIx8/nffLJJ/nzn/88IqfZydmZODPetA/VFso3NrLf6G55ebkkhHb55ZcPePzu34/m5maKi4oIj4iwumKtTCZjwYIFfPXVVxw7dqxPOYVMJsPf3x9/f3+gS606Ly+PnJwcfHx8hoxC33rrrTz55JMcP36ctLQ0pk6dOqRNBgG1q6++2rhFDP5bh25YTNJotVTX1PTYZ6g6dAOlpaV88803kkMRFhaGm5sbOTk5PRarVI6OIJPR0dFBcnIy06dPx2mQVFJLKIw7OzuzcuVKXnrpJf7+979zzTXXsHz5crOfZyAM19Knn34a6Ol0G8bP29tbmusdHR2SmvNYSi03FzKZjIiICCIiIjh58iRH8koH3X+w1n2mYMr11NHREW9vb2prazl9+jSXXnrpiM5tLPZSH63T6Xj00Ud55513AFhy2WUsWLAAvV5PQWEhzc3NuLm54ezszHvvvUd2djaHDh3iYhO6fgjGDiLUJhAILlgMqeWmCKhVV1dz5PDhnhHSw4dttubOQWmcYFdJScmQEfHenD17tt/6XYPDPW3qVF566SWbSqcbrghXZ2enlFYbERExoLhQ7wh6UVEReroE+2yBmJgYwsPDmT9//pCfS2hICC4uLj1aPg2Gv78/1113HYAkjDYY7e3tfPTRR4BxAmoGZDLZkE5tVFTUkP/f/v37ef/996mtrcXDw4M777yTyMhI/P39+wplzZvHzJkzcXFxoaOjg9TUVNqHSCW1BJGRkaxbtw7oqqMf7euOWq0mJycH6N/p7q7XYEgt9/LysrpzZOsYK6xYXV2NRqOxsDU9CQgIAOD06dOjFqVNTEwkJCRk0Dns5ORkVOmLpVCr1dx666288847yGQy3nnnHRYkJoJMBr1q/gMCArjq5wWy3bt39xGHE1wYCKdbILACjY2N7Ny5c9CejgLzMNBYG1ac5XI5N910k1HHkgTJekWPO9Rqs9Xc6XQ68vPzOX36NPn5+SNqEdTW1kZhkXFp40eOHOG1115j+/btRv0fWq2W7UlJg+6Tn5+PVqs16vyD0fFzixtTFwX6w1gRLoODbSAnJ4fOzk5cXV0HFEIbSLAOujIVbGFhRiaTceedd3LppZf2m+7ffaxlcjkTo7vaupWWlhqVDWFIMf/kk0+GzLIwRJjDwsJYtmyZ0f+DTqej6ue0ZYVCgUKhwNvLq0fv3sEWOTo6OkhLS2P37t3odDri4uJ46KGHiIiIkPbpTyjL0dGRadOm4eLiQnt7u9Uc7xdffJHJkydTXl7OAw88MGqOUGNjI0lJSQQGBuLm5ibNg46ODpqbmwF6tAQzpJYbMicEA2PsokRJSQkHDx4kNTWV4uLiYdX2m3o99fPzQy6XU1NTY3RbrJGiUCh44403gIFT4dvb2/nXv/5FSUnJqNjUnc7OTlasWMGXX36JUqnkiy++4KGHHgIGLl0KCQ0lYcoUdDodGzdutJmFWMHoIZxugcAKaLVa6uvrzeKQCAZnoLH+7LPPALj88suNSmXT6XRknTs36D5Z586N6AY4MzOTN954g48//phNmzbx8ccf88Ybb5isLg5d7Xvef/996uvrh9QPmD59Or6+vqjVao4ePcrbb7/NJ598wrlz5wZ0+pOTk4dcNGpobCQ5Odlk27uj1+tpbGyko72dxsbGETsYxqbbd69fbG5ultL7W1tbOXLkCO+++y6fffYZmzdv5tChQ+h0Oou0sbIEg0WP9DodHe3t6H/+3L28vRg3bhx6vZ7z588Paf8VV1xBcHAwNTU1fP/994Pu211ArbvDPBTFxcW0tbfj6OjIvLlziYmJQalUEhMT01WTDgPWoFZXV3PixAnq6upwcHDg2muv5cYbbzSq7RvQr+NtjsUgU3B2dubTTz/FwcGBzZs38/HHH4/KebVaLe0/j3tsbGyPNHInJyc8PDykiG17eztNTU3IZDKrRiPthbCwsCE1LxQKBS4uLpIAZE5ODseOHePo0aNkZ2dTV1c35CLtcK6nCoVC+gxPnz494HHN3eZs1apVbNy4sU97ydDQUF599VUmTpxIQ0MDH3zwgVGtK81Fe3s7n3zyCUlJSTg7O/P999+zdu1aYPBOIWfOnGHe/Pn4+PjQ0NDAli1bRs1mgW0gnG6BQHDBodfrpdTy22+/3aj35Ofn0zlEWl+nRjNskaXMzEw2bNjQx5FtbGxkw4YNJkVJ8/Pzee+996ipqcFJpWLmjBn9qlOrVCri4+NZsWIFjzzyCLfddpukrp2bm8uXX37Jm2++yf79+/tEVIyNeIwkMmKIGGRlZQGQlZU14vY5xoprdY/YnT9/Xvpbr9fT0dFBWVkZ2dnZpKWlkZ2dTWFhoUXaWFkKvV5PVlYWZ86ckRzsgZgwYQJKpZKmpqYhBY6USiV33nknMLig2pkzZ0hOTkahUHDvvfcabXdDQ4PUJzoqKgqlgwPuPyviu7u5ERQUJC2iZWZm0tnZCXQ5jFlZWWRkZNDZ2Ym7uzsPPvggM2fONLn8wdHRkalTp0qtn6zheM+YMUNSeP7FL37RJzPD0nRPLffw8GDu3LkkJCRI27qnlhs75y5k5HL5kK37YmNjmTNnDnPnziU6OhofHx/kcjltbW2UlJSQlpbGwYMH+eqrrzh58mSfzJSRXE8NKebp6el9HHtLtjlbtWoV+fn57Nmzh88//5w9e/aQl5fHE088wfHjxyVF/6SkJDZs2GDxedjc3MxHH31EUVERXl5e7Nq1S8rSMWbhtbCwkNWrV6NQKDh79ixHjx61qL0C20IIqQkEggsOQ2TAxcWFlStXGvUeY2paoUudVKVSIZfLpdRXuVyOn5+fVIeqVqs5cuQIjo6OODg44ODgIKmoD0R2dja+vr5DOggnT57kP//5DzqdjpCQEGbMnImjoyOubm74+voOKAAnk8mIiooiKiqK+vp6jh8/zsmTJ2loaGD37t3s3buXhIQE5syZQ3BwsNFCN8bu15vuYmfdBbwMYmf++nbi4uJMPq6rq2sPEa7+UKlUTJgwAehKC29sbEShUDBr1ixkMhmdnZ1Mnz6d1tZWWlpacHd3N1qIztQ2VpZCq9WyZcsWXCNj8fHxITAwcMB9HR0diYiIIDs7m9zcXPz8/AatQb377rt58cUXSUpKoqSkpE+kCv4roHbttdea1Ld8586d6OTOeHp6EjBA2nJ0dDQlTdUEBATg4OBAc3MzmZmZ0sJRaGgokZGRI+oXr1KpmDZtGqmpqbS1tZGamsq0adNGpNBvKr/+9a/Ztm0bycnJ3H777fz0008mZQyMhN49uqHnPBWp5aZjaN2XnZ3dU8RPpSI6Oror2qxtxcnJifHjxzN+/Hi0Wi11dXXU1tZSU1ODWq3m7NmznD17Fui6/obMnI9cLpcWZvq7ng4kHmnAx8cHJycnHBwcaGhokBYlhxKljI+Px9fbZUTjolAoWLRoUZ/tHh4erF69mvDwcHbs2EFmZibl5eWsWbNm0OvZcKmrq+OTTz6hrq4ONzc3fvrppx5ikcYuvDo7O3PllVeybds2du7cSWho6LB/JwX2hXC6BQLBiBiuqrY1MUS5r7/+erP3jdZqtf3W2XVPX21tbeXHH3+UngdMmYV71GQGq+rr6GilrKyMgICAflWk9Xo9O3fu5NDPPbOnTJnCihUryKofum6sv8/Q0Be4qqqKkpISjh49SkpKCikpKYwfP55Zs2bh4eFBU2Mj/SUSyui6KUpMTBzy/P39L8b0Fo+JiTGp9d7p06c5lltCSGjooJHB6OhoZDIZGo2G3NxcAMLDw6XP0MnJqc932thFGVuJ+imVSubPn09aeR1FRUUEjhvXJQA0AMHBwZSXl9Pc3Exubi4xMTED7jtx4kQSExNJTk7m3//+N+vXr+/xeltbm5QSbYqAGnQJwTUVVjAxOnpAe+VyOdOnTwe6UtFzc3PR6/WoVCpiY2NHrP5soD/He/r06UN+xoN1PzAFhULBv//9b6ZOncqBAwd4+eWX+4y1AXNfpw1Od0dHB3q9vof9bW1tNDc3i9TyYeDn5zfo4mhvDKnfhnFubm5m3LhxnD9/npKSEsrKytD+nBkyGOfPn8fR0XHQ7+HKlStxc3Ojra2NtrY2dDod53+Omg9EdnY2k2YnWFRMc86cOYwfP56vv/6auro63n//fZYtW8asWbPMdo6qqio++eQTmpqa8Pb25vbbb++z8GTKwuucOXPIy8sjMzOTr7/+mgcffHBUF+wE1kE43QK7RqfTUVhYSFNTE+7u7oSFhdlF/2tXV1fmz58/oAKywHz0HuvOzk6+/PJLwDTV8vDwcM7VDV1brVQqCQsLw9XVFa1Wi06nQ6fT9fislcr/b+/N45sq0/7/z0nSNk3TfS/dS0s3aKHsqyCrIIsWVJTFZVxmnJ/bM47jODozz+g4wzzfZxz3R8cFRUURcRlApAJSkK20pXTf971Nurdpcv3+KDk2bZqmaU6bI/f79coLknPyydWTnHPu676vRYbExERoNBr09fVBo1CYVRCnsLAQhYWFcHBwgJOTExQKBTw9PeHs7Izc3Fze4b7hhhuwdOlSg4FOU1PTiCsoI61ESCQS+Pr6wtfXF9OnT8fFixeRnZ2N6upqVFdXIyQkdOQcPwALFy40WHnT6XTo7u7mV4j1D/1z/b86rykGdmq1WrS1tRnk5et7iw8ufmWKq1ev4osvvoB33CxoNBrTK0rXjkd5eTn6+vqgUChGbWcVHBwMh6uFo66gm9PGaqKYPXs2co6eQFdXF5qamwdWsO3tERgYBLshjiPHcYiMjER6ejrq6urg5+dn8m+55557cPr0abzzzjt46qmnDH6Ln376KVQqFUJDQ7F69eox2TxjxgxI/dTgBl3njdmsX/HTV9d3cXFBfHy82VWizUXveGdkZBiseI+EqfPQEgc1NDQUL7/8Mnbv3o1nn30WK1euRGdnJ2pra+Hv748lS5ZYZfVbq9Xi8uXLePXVV9HQ0IDIyIECe6dOnUJHRwfCwsL4iAV9aLm7u7vVj/f1gL6InyUolUosW7YMy5YtQ0dHB4qKipDb1A7VoLQWY9fTvr4+pKenm9Q2VlvEJ960Y2utNmejERAQgAceeACHDh1Cfn4+vvnmG5SXl2PDhg3j1q6qqsJHH32E7u5u+Pj4YMeOHUYn680thqeflNu4cSNqa2vR2tqKr7/+mrWRvQ5gTjdDtOTm5uLo0aMGObAuLi5Yu3atRWGnE4m9vT2CgoIm24zrgqHH+tixY2hqGgg9NdVreTBEhPxRiqgBA6u7+tVRHx8fREREGF31UiqV2LRpE//8bFElMjMzR9W3s7ODRqNBb28vent7+UJRxcXF6Ojo4Fc99C2NvL29oZU5obW11aK+1IMJDAxEYGAgVq1ahcuXL+PSpUtw9PQ06bz29vXhvffeQ1dXF/8wp8COj7OhLUQ0rGI8YP7KQnZ2Ng4ePAgigp+fHyLCwwGOG3lFSTvg/Our4upXvk2hz8k0dpz1mKMzkTg4OCAgIAAVFRWoKC+Hl6cnpFIpXFxdjO7v4uICf39/1NbWorCwkA+3N0ZycjJ+/etfo6ioCKdPn8bSpUv5bfoCar/4xS/MniQdvJo62OEmIrS3t6Ovrw860sHV1RUtLS3Iz0mHRqPh0zza29vR2dkpyOB/8Ip3V1cXMjMz4e7uPmxgLlQo7s6dO/H111/j888/x8KFCw1aSgUGBuKll14a1pN9LBw8eBCPPPKIQZujm266Cf/6179QW1sLb29vA+eahZbbBkqlEomJiVDlFBs43SNdT+3s7Eyejy4uLvz7icjsQrATlVIjl8tx++2348cff8Tx48eRlZWF2tpabN261aDewFjQ1zbRaDQICgrC9u3bIZfLje5r1sTrtfuM3t7k5GS88847yM7ORnh4OGbNmmWRnQxxwJxuhijRF50air7o1LZt22za8e7p6UF5eTlCQkJGvIAzrMPQY60PLb/jjjuMhmkb48SJE7h48eKos/rRMTFwUDegqqoKDQ0NaGlpQXh4+Kj5Wq6urmblGc+bN48PX+/s7ERrayvq6urQ398Pe3t7rFixAt99951BKKlPfBJGc/PMzRcHBgZyS5cuRWBgIL5Nz4G3tze8vLygVql+cl6vtVjS6XR80avBODo6QqFQwMnJiV+x1//fyckJ1RoJ3wsYACQcBwe5HL09PdANctrNWcHLycnB559/DiJCYmIiIqOi+LBkUytK+krj3t7eBkXVTGFuTqYtEThlCqqqqtDe0YHWVhWcXZyhVqvh6upq9PwICwtDU1MTPykxUgSAUqnE7bffjrfffhvvvPMO73RnZWXhxx9/hEwm49uLjUZ7ezvef/99LFmyxCCHUr9qrOnr438fHMdBq9PBU6uBUqlETEwMKioqUF9fj9zcXMyaNUuQME65XM6veHd1deH999/Hrl27eMfb3Or2loTichyHm2++GZ9//vmwHs7V1dVITk7GiRMnjOZhj8bBgweRnJwMIoKrqysWL16M1NRU1NTUIDk5GVu3bkVycjJ/juivTSy03HYYOvE70vU0NjbW5KTUpk2bkJubi6+++gqhoaGYN28ejqRdHfPnC82CBQsQGBiIAwcOoKmpCfPmzcNrr72G3bt3j0knJycHBw8ehFarxdSpU7Ft2zaTkRsSiQQBAQEmU5ekUqnBBOKUKVOwcuVKHDt2DEeOHEFgYCBftI7x84M53QzRoa9UaQpL8j0nEn0Yoo+PD3O6BWbwse7r68OhQ4cAmB9anpqayre9cnF2Rlt7OyQcZzBYGexUeXoq4ePjg4KCArS3t6OgoAD19fWIioqCQmF8FYvjOLNXSWUyGVxcXNDb24vm5mbodDoolUrEx8fD1dUV9vb2aGhoQGNjIx/mOdrasiUhgJ2dnQb2u43gmM6ZMwfR0dG8Q+3o6Di6s9zYhqqqKt5xlUilUDo5QaPRQDfIqTh06BBaW1sxf/58o5q5ubm8w52QkICbb74ZeS2dw/Yb9vGNjXyrNX1BNXMZa07mZGNnbw9/f39UV1ejoqIcAVOmoLGhATqdDl5eXsPstrOzQ3h4OPLz81FWVgZvb+8Rndh77rkHb7/9Nj777DP861//gouLC7/KvWnTJrOLHR0/fhzNzc24ePEi73QPLbSn/33onU5PT0/ExsZCIpEgKioKnZ2d6OjoQE5ODhISEgS5N8jlciQmJiIjIwNNTU3Yu3cvdu3aBScnpzFVtx/rarxWq8UzzzxjdJs+suSrr75Cd3e3yd/h0H7HOp0OTz75JK/h4eGBnTt3Iicnh6/Cf/ToUdxzzz38BI1+ldvDw8PsSU2GsAyd1DV2PTU39cXDwwM9PT0oLCzEhg0bRp0sth+0smsplqQRBgUF4YEHHsAXX3yB7u5u3H333fjhhx/wyiuvjHgfHszly5fxzTffgIgQHx+PzZs3j3rf6u/vR8O1CW+JRGJQ5d3e3h7a/n50dXcjPz/fYFFo/vz5KC0tRWFhIT777DP84he/GNU+hjhhV0SG6KioqBi1P/BY8z0Z1wcHDx5ET08Ppk2bZlaRlQsXLiAlJQUAMG/ePJReC2dOSEyETqcb0alSKpWYOXMmqqurUVZWBrVajbS0NAQHB4+YVjDqKumgsNPy8nK+cJenpydiYmIglUrh6uqK2bNnG+imZBXwLWJMMdYQQHPz12JjY8d8HpozCeHh4YGWlhYcP34c6enpWLt2rUHLHSJCeno6dDodZsyYgY0bN5rlaGm1WpRcW2UPDg62aFJsPDmZk0FQYCBampvR2dmJgoICuLu5oaCgAMXFxUZz/v38/FBXVwe1Wo3i4mLExsYa1Z0/fz6io6ORl5eH//7v/0ZsbCzeffddAOYXUKuoqMCVK1cAAOvWrQPHcWYV2tMX8gIGBsBxcXFIS0tDW1vbwIrytX7e1ka/4l1cXIzGxkbs3bsXO3fuFLS6/enTpw1Cv43R3d09al/g119/fcyf3dbWZmCzfqKPrdbZDmOZ1B0NX19f+Pj4oKGhAfn5+aPqchi4plo6ATOeNEKFQoHt27ejq6sLzz33HN59911cunQJn332GV8IUqvV4vTp03wNhMWLF+Mf//gHKioqAAzUvbjpppvMOjY//PADOru6YG9nh6TZs9HV1WUwRlCr1LiSdQUNDQ1QKBQICQkZOEYch82bN+ONN95AU1MTjhw5gvvvv9+i48WwbZjTzRAd5g5ezN2Pcf2gDy2/6667Rr2JZmRk8G28lixZguXLl+NUbgk6Ozv53DZTcByHwMBAeHl5oaioCM3NzSgrK0NDQwOmT5/O33AHY3KVVNsFnU7Hr5wDP7U+MvW3DK6aboqxhgCaWzgsODh4TLp6Bk9CDM4d1E9CLLllPTIzM/lV0H379mHatGlYs2YN3N3dwXEctm7digsXLmDBggVmr2yWl5fzbV2ul7oL7R0d6O7pAWB+O6HIyEikpaWhsbERLS0tRitfcxyHOXPmIC8vD//4xz/416VSqVn9ynU6HQ4fPgwAmDVrFt96TK1Wj3nVWC6XIyYmhs/zdHZ2FqyrgqOjI3bt2oX33nsPDQ0N+OCDD8xOd7IkFLe2ttas/fz9/U1Olg0tOlVdXT1qcS3gpzSPjo4OdHV1QSKRjKsdG8P6jHY99RpDLYHp06cjJSUFWVlZmLP+FuOTxfb20Op06O3rQ05ODqZPH3vahDlphB4eHiY1OI7DM888g4ULF2L79u3IysrC7Nmz8dZbb8He3n5YrQKlUomOjg489NBDWLp0KZYvX26WrbW1tUhNTYV33CxMjYyEvb39sHPZzd0NkZGRaM7NQFlZGRwdHfnJKYVCgeTkZLz33nvIyMjg01MYPy+Y080QHebm45m7Ese4PmhoaODbdN15550m983JyeFXhebNm4fly5eD4ziLVm/kcjni4+PR2NiIoqIidHV14b333sPMmTOxatWqYU7xSKukfX19yM7ORltbG19J2pzenubmi481BNDcwmHjCePVT0I0NTWhsaEBUVFRfMgzx3FITExEdHQ0Tp06hfPnzyM/Px+FhYVYtGgRlixZAjs7uzEVkOru6kJVVRU8AERERNhseoo1MWfV2FjOv5OTE6ZcywcvKipCZGTksNWsgwcP8hNdg9Fqtdi2bRsOHDiAW265ZcTPTUtLQ319PeRyOW688Ub+dXNXg4fu5+HhgbCwMJSWlqKyshLTp08XrK+1p6cn73jX19ejvr5+1JoQ9nZ2FoXiukbGY9vDT5jeSduFNWvWGJ3s0/Poo48aPD958qRZTofewdavcnt4eExYv3CG+Zi6no6l1kR8fDxSUlJQVlaGGb29I04Wd3Z2IiM9Ha2trfw1wlzMTSO84447zHLmV6xYgYyMDNxxxx04efIk7rjjDqP7dXR0AAASExPNdri1Wi2+/PJLvgaIqQKC/v7+UHQ0o7KyEvn5+ZDL5fwkfnBwMJYvX47vv/8ev/zlLzFv3jyDOgw5TaMvJMV6sXGvLfPzH1Ewflb09PTghx9+GHU/FxcXi1fYJgI7OzsEBASwdioTgP5YHzlyBESExYsXIywsbMT9CwsL+TzgmTNnYvny5Vapvurt7Y05c+bwjnJ6ejpeffVVXL16FRiUH05EUKlUaGhogEqlAhGhs6MD6enpaGtrg0wmw4wZM8xyuIGfQgtNYWlVbf3qydCJMAcHB8TFxVmlkBLHcXB1dYXS2dlobrRcLseaNWvw0EMPwdfXFzqdDqdPn8bLL7+M7Oxss6qlAwCuOZ9EBE9Pz+tmpW7oqjFdW52iQfmI+lXjoYSGhsLBwQHd3d04c+aMwTatVotHHnnE5PF/9NFHR6yA3NnZyU+SrVixwiAPc+gEkjGbAeOrxsHBwQgLC8PMmTMFdwy9vLwMHI3RPk9HNOoKvjFCgoPh4uIyYsFEDgOTb2O9Jy5ZsgSBgYH8OdfV1YVLly4ZtDf08fHhHXkWWm77jHY9NQc3Nzf+t6TP4ddPFvv4+MDtWiFNpVKJmGupJzU1NaOmQAzG3DRCcyJm9Pj5+eG7777D008/Peq+paWlZt87UlNTUV9fD0dHR0SOcq8FBopRenl5QafT4erVq+i5FmUEAIsXL0Z4eDi6urqwbds2dHd3m2UDQxwwp5shGrq6urB3715UV1eP6qyuXbvWpleplEolFi9ebLTXI8O66I/1e++9B8B0AbWysjJ8+umn0Ol0iI+Px4YNG3gHzliP0rEik8kQFRWF3bt3w8vLC52dnfj888/5G29TUxPOnz+PzMxM5ObmIjMzEz+ePYvL6eno6emBo6MjZs2aNeZ84ZGcY71N48k/9vLywrx585CQkICYmBgkJCRg3rx5Vq1cbG9vj+DgYJOht2q1Gk1NTQAG/qb29nYcOHAAH3zwAT8wNEVTczNaWlsh4ThERERYzXZbZ+iEklanG+jhO8SBNTbxJJVK+WN1+vRpvoWd/rmpQTYRobKyki9SOJSCggL09PTAz8+Pr79AREhNTUVJScmoNpuK3ggODp6wCc+VK1fCw8MDDg4O0Gq1sLOzG+YccxiYHOzv78eVK1eg0WjG9BmcRIJ1a9fyWkO1gYF74lgdLKlUipdeemlAh+NQX1+PPXv2oL6+no82ef3118FxHNrb29Hd3Q2JRDJqyC9jcjHnejoa+hZco11bPT09+WtEcXExmpubzdIXqgaCTCbDqlWrRt1PrVbzed2mqK+v5xeC1q1bBzszjinHcYiOjoazszM0Gg2ysrL4ApAcx2HLli3w8fFBVlYWHnvssVH1GOLBdr0SxoSi0+lQVlaGrKwslJWVGVRdtAU6Ozvx/vvvo7a2FgqFAvfeey+2bdtmNLdWDPlkOp0OPT09Nnecf47odDqkp6fj6tWrsLOzw9atW43uV1VVhY8//hj9/f2YNm0aNm/eDJVKhXPnzqGzs9OqkzghISF44IEHcMMNN0AqlaK5pQUXL1xAdnb2sJUuTX8/dDodnJycMGvWLLNztIcy1DmePn06HOVy9Pf3G7TnsgRjqxzWhIjQ398/4spDcXExPvnkE2i1WkRHR+OJJ57A0qVLIZVKUVpaijfeeANHjx41WFHQ66pUKtTV1aHwWrG5wKAgi4+xGDE28JYY+f5GGqDrW6pptVo+mgQwP894pP1mzpyJ3bt3Y8OGDZBIJOjr68OBAwf4wobuQyaKhtpsbvRGeno6vvvuO7NsNQeNRoOamhr+uZOTEx5++GHcc889kEql0Gg0w7oJ0LX32dnZobu7G1lZWWb3QNYTHRODbdu2wXnIPdHFxQXbtm2zqF0YANxyyy04cOAApkyZAqlUCmdnZ0ilUr4lkz49QL/K7Xmt1zvDdhntemoOsbGxWLx4MWLM+F0FBgbykVm5ubl8CLcpzE0PFLIGwmiOv06nw5dffgmdTodp06YhPj7ebBukUik/Ed7V1YWcnBz++1Aqldi3bx84jsObb76J/fv3m63LsG1YTjdjXNUhJ4L29nbs3bsXTU1NUCqV2LlzJ7y9veHr64tp06bxrSSUSiV+/PFHFBYW4tChQ7j33ntt9uavVqvx3XffYdWqVWb3AGZYhlqtRmFhIYKDg5GQkGB0Faaurg779u1DX18fwsLCkJycDKlUimPHjkGr1SIiIgJRUVHIbR59sGAuMpkMy5YtQ1xcHI5l5I4aJtff3z/u3/PQfPFp06KRkZmBuro6+Pj42OxvsbenByUlJQgPD4d8iENcUlLCO9zTpk3jv7vly5cjMTERx44dQ15eHs6fP4+srCwkrdsMP19fNDU3Dyv+w2HASbqeGJrzL5PJ4O7mhlaVil99Ga3tT2RkJM6fP4+ioiLk5eUhJibG7PQHU/vpw5ZbWlqwf/9+NDQ0QCKRIDIyEgEBAXyfbq1Wy9sslUrNLgxVV1fH127w9fU16AFuCSqVCnl5eejt7YVMJuNXA/U9q6USiUlnWr/i3d7ejuzsbMTHx49psi86JgbTpk1DeUUFOjo6oFQqERIcDE4iGVd/+FtuuQWbNm3C999/j9bWVmg0GiQkJBiEzuudblP5rAzbwNT11FwUCgVuvPFGs/KMgYFrRE9PD1pbW3H16lXMmjXLqMOs1WrBcRyCr6VMmAoxd3JysqgGgrnXptEc/7Nnz6K2thZyuRzr168f82Szg4MD4uPjkZGRMSzvfeXKlXj66afx/PPP4xe/+MVAVxJXlrYhdthK93WOvjrk0AubvjqkNUJqx4Narca7776LpqYmuLi4YPfu3QY3dYlEgtDQUEyfPh1hYWG4+eabIZfLUVtbO2LYIuP6QavV4uLFi/xzY8VTmpqa8MEHH6CnpwdBQUG4/fbbIZPJUFxcjPz8fHAchzVr1gjWZ9nLywuhIaGj7jdSXu14cHVz5StCi7Haf2lpKR+dEBUVha1btxpMTLi7u+O2227DXXfdBU9PT3R1dSE/Px8XRogqIAxcE/Vh6tcD5uT8a7Vag77sQ3F0dMTChQsBDBQ36uvrG5YPbOxzg4KCsGTJEoPXS0pKoFKp+OdFRUV466230NDQAKVSiV27diEgIADAT9Eb+vZfUVFRY0pt8PPzw+LFiwEAX3/9Nerq6sx631CICKWlpcjMzERvby8UCsUwGyoqKtA3Sth4n0aDsLAwSKVS9Pf3j3m1GxgINQ8NDUV8fDxCQ0MHHG4rIJVK+XaEOp0On3/+OV/ZvK2tDT09PZBKpTYfZcaYHDiOQ2xsLBQKBXp7e3H16tVhv++SkhK88cYbuHz5MiQSCdZeS5kYicGObkVFBaqqqsyKHjTn2jRaDYSuri6cPHkSALBmzRqLC/cqlUo+CqWmpgbV1dX8tj/+8Y9YvHgx2tvbcdttt0F7bRKUIV7YSvd1jLnVIadNmzYp+dGtra3Yu3cvVCoV3NzcsHPnzlFX4pydnbF+/Xp8/vnnOH36NKZNm2b2rObPgaE9J5csWQKpVMq3mDKFUO1zJouDBw/ikUcegZ2dHV588UUAwOOPPw6ZTMaHROp/Y11dXfD398f27dthb28PrVaLb7/9FgAwd+5cwVdv+jSWVWO2BmFhYZB7Ks1qg2ZrqK6txkZGRg5zuAcTERGBhx56COfPn8fVehXfHmskjFXr/jkzYjshe3tIJBJoO7ugVqtN1qBYsmQJsrKyoFKpcOrUKaxatQovffQ53/JncCCr/qhu27bNYIKjv78fmZmZ0Gq1SEhIgFqtRmlpKXp6ehAYGDgQPu3sbLC6xnEcnJVKNDY0wFmpHPN3tnz5ctTW1qK4uBj79+/H8m07IBtDvnd3dzdyc3P5SSt/f39EREQMu++YO6nFcRwSEhKgUChsNlJLH56rX5XTr3J7eXnZdC0VhvVpaWlBfV0dpgQGjnoP0Ud/XL58Ge3t7cjPz0dsbCx6enoMFnnOnz+PpKQkxFxLmTAViVlfX4++vj6Ul5dDp9OhsrISwcHB8Pf3H/G3qK9VkJycDI7jDMLs9dcPUzUQiAj5+fnQarWYOnUqEhISxnTMhuLl5YWIiAgUFxejqKgIcrkcvr6+kMlk+Pjjj5GYmIi0tDQcP34ca0aZiGDYNuzqeB1jbnVIc4pJWJvm5ma8++67UKlU8PDwwO7du80OfY2Li0NsbCx0Oh2++OILPkTy587BgwcRGhqK5cuXY/v27Vi+fDlCQ0Nx8ODByTZtwjl48CCSk5OHFXKqqalBcnIyDh48iPb2dnzwwQdob2+Ht7c37rrrLsjlcgDApUuX0NjYCEdHRyxbtkxwe83NSxtP4ZuRkEqlonS4gYG83zvvvBPbtm0b1q5qKFKpFAsXLsQ0M3IQhYgqsHWMrhrPn4+kpCRER0fzEREjYWdnh3Xr1gEAzp07h4aGhtHzjIekL5WVlUGj0UAul6OqqgolJSV8F4Fdu3YJ0gZSIpHg1ltvhZubG1QqFXJzc83OdW1sbERaWhra29shk8kQFxeHqKgoo86yubYXFBSgsrISLS0t/ASIOTmwQqPT6fhcdSKCm5sbPDw8QEQGTjfj+qKhoQENjY1mTewDA90m4uLiIJFI0NjYiIyMDFy8eBG5ubngOA5z587Fvffeyzu8MTExeOSRR7Br1y7ccsst2LVrFx555BGD1Ec7OztERkZCLpejr68PRUVFOH/+PKqrq0dc+R5cq2Aw+loFpmogVFdXo62tDQ4ODrj55putMjk7NO9dH3UTGBiI999/HwBw7vx55OXljfuzGJMHW+m+TtFqtSi4VjhoNCY67LShoQF79+5FZ2cnvLy8sHPnzjENtjiOw0033YTy8nI0Njbi5MmTWLlypYAWTz56J3PoYLG6uhrJyck4ceKExYV0xIapVkVEBI7j8Mgjj+DJJ59Ea2sr3N3dsWPHDoOWRPoqzCtWrJiQolpC9dIeK93d3SgvL0dkZKTNrLIREdqvOR3tHR3o7euDYtB3MlpotDE9cxAiqsDWMbZqLJXJ4DkoCkYfam5soiYqKgrR0dHIy8vD4cOHMXf9LabzjAfR2dnJO3U6nQ6NjY18P/rExERB/25HR0fcdttt+Pe//42W1laUlZWZbCuoRyaTQavVws3NDdHR0Ua7A+gJDg6Gw9VCk+c4B/BObGNjI6RSKRwdHdHR0YGIiAgEBgZa8ueNG33dF33NC2AgvDYvLw9OTk58DjurWn794ePjg/r6ejQ2NmJqRIRZ6Qyurq4IDAxERUUFP7kZGhqKtWvXGo2406cRjgTHcfDz84Ovry/q6upQUVGBnp4eFBUVobKyEomJiUb70+trFYwlOrC7uxulpaUAgNWrV1t1wnpw3vvHH3+M++67j4/efOKJJ1DZC3z55Zfw9fWFWq02eT1l2CbM6b6O0Gq1KCkpQU5ODnJzc83uBzqRFbbr6urwwQcfoKurC76+vtixY4dFhY2cnJywYcMG7N+/H2fPnsW0adMQFBQkgMWW4erqii1btljFsTHHydSnCVwP4bJDWxWVlZVh9+7dfOVqIkJVVRXS0tKQkJBgdFJn3bp1SEhIgJ+f34TYrM+rzc7OHnEfS3tpj4WrV6+iq6sLMplszM6sEOgLZfX29oLjODReC0V2cHDAVNeFFl0bJjOqQAw4yOWIjo42OojTarXIyspCe3s74uLijDpZa9asQXFxMcrLyxHS0ABfX18+z9gU+h7pHMehp6cH9vb2iI2NNWuiyZTN5uLn54eNGzcitaAcEokERAS1Wo2+vj6+kBzHcejXaPgYQXd3d8yYMcOsiv0SiWTUczwmNhaB9gNOd0NDA3p6evhV7uLiYjQ2NiIoKAienp4jRnaMZLel6Ou+6MnPz+fHBJ9++ikiIiJgb2/PQstFhDXOFz3u7u6wt7NDn0aD1lYVPDzNm3jx8vLioyg5jsPSpUvHneLGcRz8/f3h6+uL+vp6lJeXQ6PRmIyS5DgOoaGh8PT0hLOz86jniv737+7ujpkzZ47LXmO2xMbGIj09HW1tbfjkk0+we/du2NnZ4YUXXsBT/+9VVFdX49VXXzVIA3JxccG6tWuHRQ4xbA/mdP/M0Wq1KCsrQ3Z2NnJzcw1a5jg5OUGj0Yy6onPo0CFUVVVh2bJlgvaVrq6uxocffoienh74+/tjx44d41pljI6ORkJCAjIzM3Ho0CE8+OCDE9abdTQkEonVBijm9MPV95w0Ntv7c2NoOxAiQnd397D9NBoNduzYMWKPan2xpolicF7t4AkxBwcHs6sxj5epU6fiypUrqK6uhre3t+Ar66ZoamoycFAGTyr19vaipKSErw49FmwlqsBW4TgO3AiTgRzHQSaTQafTITs7G7GxscMKZ7m5uWHp0qVISUlBSXExPD08Rs2Rbmho4IunERFcXFwQFxdn9sSHKZvHwvTp09Hr4o2u7m6cP3/e8Dy0t4eziwtUKhVWJ8bwqShjqfg/6jnu5QWltgtKpRJhYWFob29HY2MjampqoNVq0dbWhuzsbHz11VeIjo5GbGysQU/5wZNUQ7U9Lbh+GKv7MnQSvqSkBNHR0axquYiw1vmi1/L28UF1dTUaGupHdLq1Wi1UKhV/vXB2dkZcXBwaGhrQ2NiITz/9FPfdd59VCvFJJBLe+W5vbzdYjf7mm28QEBCAhIQEFBQUjJgvbmxCsbq6Gmq1GlKpFFFRUYJMgstkMsTHxyMnJwc1NTX44osvsHXrVtjb22NmYiKqq6uHFaBrv1b4eNu2bYhdMtfqNjGsB3O6RYZOp+NbZDk7OyM4OHiY86bvua13tAc7HE5OToiNjUVcXByCgoKQn59vMIs9FH9/f9TW1uLSpUu4cuUKFi5ciAULFlh9FaiyshL79u1Db28vAgMDceedd/KDmvGwdu1alJSUoKWlBcePH+dzDieb9vZ2XL58GbNmzRp3nqK1ek7+XBhawMjPzw/33HMP3nnnHYPqxLfeeqtBDiIR4dSpU0hISJi01lleXl7w9PQ0vlI1jpY/5uLu7s6f8/n5+Zg9e/akrF4REYqKivjnUokESqUSHR0d0F4b9B8/fpzPDRwLthJVYKv09faitq4O/n5+sB8SLi2RSBAXF4fc3Fw0NjYiOzsbMTExwxyuBQsWIDMzE30aDUrLygxaSw37vL4+5Ofn88/9/f0xderUMX2vpmweK13d3UZ/G719fei9FmnR1NRkcai3yXN8CM7OznB2dkZYWBiysrLQ2toKYOCYXblyBVeuXIGDgwOiFt8IBwcHo/VXent7kZ2dDc/osDHnXA+t+2JnZwc/Pz/U1dVBc60SOxFBKpXabLtBxnCseb4AAyHm1dXVaGxshJu7O+Ry+U+/aSLU19ejpKQEGo0Gc+bM4RdTvLy84OHhgd7eXvT09OCjjz7Cvffea5DqNR4kEonB5Gl1dTXS0tKQlpaGlJQUdHUNv6fqO/ckJycbnC+Dw8rDw8OtMj4dCX26y969e5Gbm4uUlBQsX74cP4zQkYcwkJpy9OhRbFqYZDOpYYzhMKdbRJjqpz1t2jSUl5fzjvbgi4lCoUBMTAzi4uIQEhJiMJgxpzpkWVkZvvvuO9TU1ODkyZO4dOkS3wPXGgPysrIyfPTRR9BoNAgJCcEdd9xhMjfOnL6QsV4DjqxcLsfGjRuxb98+XLhwAdHR0Wbl6glNf38/6uvrDYq8mVuIZGgIlrV6Tv5c0LcDqa6uBhHB0dERCQkJBlETAQEB2LRpk8H7cnNzcerUKZw/fx6PPfbYpIUXD+2lPdGEh4ejpaWFH2QMXkmbKNRqtWH/bIkE9vb2A+GQ15xufZHH0cKWjWELUQW2ik6nQ2dHx4hpRRzHISYmBhKJBPX19XzhscHXJalUiptuuglHL2ejpqYGfn5+Rq8/3d3dKL1yhf+sqVOnjlqwzRKbx6IzeLLHGHYymUU2Dmas5zjHcYiPj8fVq1fR2toKBwcHTJs2DaWlpWhvbzfr3mFJRf6hE7VSqRRKpRJSqZR3ugGYFZbLsB2sdb7o6e3tBQdAd62qNzBwLZ0yZQqamppg1zow2a1QKKDRaAzuxfqJvLy8PLS0tODTTz/Fjh07IJVKzRrrjWVd3MfHB2vWrMHp06eNOtyDGXq+FBQU8PUbJiIKLiQkBBs3bsShQ4dw5swZlJaWmix8TADUbW04ffo0brjhBsHtY1gGc7pFwtC8Kj36Wbmh4ZKOjo68ox0aGmrSOY65VuhmpBX00NBQ3HfffcjOzkZKSgpUKhW+/vprnDt3DitXrkRkZKTFN9zi4mJ88skn6O/vR3h4OG6//Xarh4BPnToVSUlJSEtLw5dffomHHnrIpFMvNoY6mUMxp+fkzwmpVIrf/OY3eOSRR0bc57e//a3BbLBGo8F3330HYKBF2PWazwsMhLdFRUUhKysLVVVV8Pb2nvDq5uYWMRtP9MZkRxWIGY7j+BoRdXV1vOM9Y8YMfp+wsDD4VjWivqEBhQUFCAsPh0aj4Y9za2srcnNz4drbCXt7e8TExEzqZBMwsLI7Wq0TTX8/1Gr1hNuqd070vcD9/f2xefNmVFRU4EJpDZqam02+X1+R3xy79bn7+j7Eo8EKqF2/NDU1IScnZ9jr+hQgAPCRShESEjJib2x7e3ts374d//73v1FeXo5vvvkGGzdutLqtdnZ2mD9/Pry8vLBv3z6T+w4+X2pqaqBSqfiw8okiISEBzc3NOH36NI4dOwaJ3+jpgeZGPjImB+Z0iwBz+mn39vbCwcGBDx0PDQ0dU4iJOdUh4+PjER0djUuXLuGHH35AY2MjPv74Y4SEhGDVqlVjnv0vKCjAp59+Cq1Wi8jISLPa/ljKqlWrUFxcDJVKhWPHjuHmm28W5HMmg8E9J0fCVM/Jnxs6nQ5arZaP4BiMPoJDq9VCp9PxE0s//vgjVCoVnJ2dsWjRoskw26bw8PDgw0irq6sn3Ok2d9JjvNEbkx1VIGb0jre+9Y+xooPhERFobGpCe0cHrly5wr8uk0rRfy0v0cXFBbGxsTYxEWruJM5kVbaXSqWYPn06n5MKDKyIlXb2j+p0A6Pb3dvbi7S0NJw7d25MfcXHu/LPECfmRIZIJRLMSZoz6vnt4+ODrVu34qOPPkJGRgY8PT3hET2+/tcjYazGizH6+vrQ09PDTx6EhYVNSDeTwSxfvhzNzc0oLS2FOVabG/nImByY0y0CzOmnDQBbt24VPBRUJpNh/vz5SExMRGpqKs6dO4fy8nK8/fbbiIuLw4oVK8ya9c7NzcWBAweg0+kQHR2N5ORkQfNQHBwcsGnTJrz//vu4fPkyoqOjTeYZio2YmBhs3bp1xDSB8fZPNaeWgC3Q3t6OtLQ0tLW1ITY2FtHR0Xwe5IYNG+Du7g6JRGIQmtzW1obU1FQAA5Mz1/Mq92AiIiKgUCgmvE2RVqs1q9iZi4vLdRO9YctERkYiODgYPj4+w7a1tbUZDWHVO9xSqRTh4eE24XAD5k/iTOY1ws7Ojne4gQHHwNzJ6oaGBjg7Ow9zHIgIZWVlOHXqFF9sValUYv78+VAqlTh06NCImmFhYTZ5L2AIjzmRIVqdDt3d3Wad41OnTsXatWtx5MgRpKSk4AbPAEEK9Jl7nstkMj6s3NXVdVImlziOw+bNm9Ha2orUgvIRjzeHgXvikiVLJtZAxphgTrcIMHfGebQcFWsil8uxcuVKzJkzBydOnEBmZiafTz5nzhwsXbqUL4Yx1GFra2vDoUOHQESIj4/H5s2bJ6TwQ2hoKObNm4fz58/j66+/xkMPPTThs5Z6HB0dMXPmTKt8vj4SQu9klpeX/9S/8VoOvyX5fHpM1RKImcQWFVqtFvX19aisrERVVRUqKyv5np96JBIJfHx8UFdXBx8fH4Oqn/rz6vjx49BoNAgKCkJ8fPyE/g22jEwmm/A2eyqVCh9//DGil64yKHam02rR0dEB3aDvb+3atWywLwAyOzv4+fmPWnF8MIMH1KWlpaipqcGCBQtGXQXTarVobW0dd6V4S2w2hjm9tG2psn13dzc++ugjKMNj4GBvj95RVrKbm5vR0tICb29vBAYG8s4Hx3Fob29HT08PPD09sXDhQsyYMYN35u3t7XH06FF0dnYaFFEDgMWLFwv3BzIEwVrnixCRIXPnzkVzczMuXLiAvLw8yOVyq9ejCQ4OhouLy6iLWXl5edBoNJBIJJg2bZpVbRgLdnZ22L59O1r2H0J6RsaI+61du5YVUbNxmNMtAsy94ExGoSxXV1ds3rwZ8+fPx/Hjx1FcXIzz588jIyMDixcvhqurK44fP2704paQkICNGzdO6MD5xhtvRFFREZqbm3H06FFs2bJlwj5bj06nQ11dHXp6elBXVzfmVWN9CzB9qw0nJyf++EokEqOF4np7e3Hx4kWEh4fzq969vb1obW2FnZ0d/3BychooWHXNOR+tlsC2bdsmLJ+vs7OTd66rqqpQXV1tUIgO+ClcWL+6Dfw0sB+Ks7MzqqqqkJWVBeD6CsEfK0SE8+fPIykpSbAUkMrKSnzyySfo6uqCvKwMc+bMMSh21n1tBU5f7CwmevILIv4ckclkZvfaHYparcbHH38MjUaDxsZG9GpH/61YI3VhPDYPxpxe2rZU2b6lpQV1dXXwcPOFm6urSadbH9XT0tKChoYGNDQ0wNXVFSEhIXB3d0doaCiCgoL4XP3BDK37olarkZKSwk/sNjY2Cv2nMqyItc4XoSJD1qxZg9bWVqh1Oly9ehUzExPR09trtd7zEokEa9euNdm5RyaT8ZNLcrncaL2ciUSpVGJWUhL6NJqRC4Cytn02D3O6RYA5s3KTHWrp5+eHu+66C8XFxTh+/Djq6uqQkpJi8j1RUVETvlJlZ2eHzZs345133sGVK1cQHR09oau1+lVj/Uq0/t+R+kICA85xY2MjOjs70dnZifT0dIOVhsTERLM+u7u72yDUs7293aBVT25uLoCBkE8nJyfceOONo36HR48exR133GH1QahOp0NDQ4OBk93S0jJsP7lcjqCgIAQGBiIoKAgBAQGws7PDSy+9ZDARoT/W+r9ff75otVosW7YMXV1dE96XW0xkZWXh7Nmz6OrqwvLly62uf+XKFXz11VfQarXw8/PjOyPoi521trby54q7u7vNOD0/R7T9/ejo6IRS6QTpGCdYXF1d+R7dmZmZ8IlPGvU9QyfOLGE8Ng/FnF7atlJob8qUKdi6dSu+zyqASq2Gp6cn2tvbDVYWHeztMTUyEiHuCrS0tECj0fArlGq1GleuXIFSqURwcDBiY2NHPLf0vY85juMjGPRV7Bniwlrni1CRIRKJBLfeeiv2nziLzs5OXLh40cDpNTgXLcRU5541a9agpqaGH3N0dXXh0qVLCAwMREhIyKSsJut0Or6wqZeXF9Qq1U+TEG5u/Hm5OCqEnZM2DHO6RYA5s3KWhlqOpf2WOURERCA8PByZmZn46quvTM4Ofvvtt4iOjp7wC0RgYCAWLVqE1NRUfPPNNwgODoaTk5Pgnzt41Vgul2PKlCl8G4hPP/0Ua9euhUwmQ2dnJ+9sAEBPTw+Ki4t5HY1GA6lUCk9PT/j4+Jh94wkPDzcoGiWTyeDp6Ym+vj5oNBrIZDL09/dDq9Wira0NTU1No4ZftbW1WaWSb3d3N6qqqngnu7q62mhImj4sMigoCEFBQUZD5nOa2pG0bjO/WiWTyeDu5oZWlYof4N8QGwGJRAKJRMLaa5iBvjhLamoqYmJijBbNGonR2hmVlZXxBe+io6OxZcsWFLUNahfGcVA4OqKuthY+3t78921Om6Sh7fUYo6PRaFBdXYXw8HCLBuSLFy+GTCbDt99+a9b+1siPHq/NQxlLL+3JJioqCtUaCfLy8tDc3AyZEYdApVIhrSQXHR0dAAbOKS8vL0gkEjQ1NaGjowM5OTlISUnBggULMHPmTL6LiH6MQERoampCY0MD1DIn+MQnwX3aDOQ0tY+pbRNj8rHW+SJkZIi+5VhBQcGwcaS+93xcXBw8x9HecaTOPVlZWWhpaeE7BtTU1KC5uRmVlZXo6uqalDS0wfnzHMfBzd192D69vb0oLCwcdTzG7ouTB3O6RYI5/bRtBX2I72jhOOPpszteli1bhoKCAjQ0NOA///kPtm7dKuiAypwK9OXl5fz/9Q4xADg5OcHHxwcKhQIKhYIvVqefrNDpdLhw4YJJB9nBwWFYuw43NzeDi/PGjRvR19eHrq4uPnfPHMZayVc/eBuci93U1DRsP3t7ewQGBvJOdmBgIORyuVmfMXi1anAet36GPHJqkEH1coZpvL29ERMTg9zcXHz55Ze47777rDLbn5OTw4emLlq0CDfeeOO136jp4jwM22b+/PmQSqW4VGF6YsROJrOZ/OihiKmyva+vL1pbW1FfX88XqdPT29eH6upqeGq7IJVK4e/vj8DAQD4PX6PRoKamBtXV1VCpVDhy5AhOnTqFuXPnYs6cOQAG2kLpr6Xubm7Q6XTgMHlV3Bm2g1CRITqdzmBMZIyioiJEzZ4+7lDzwWPQ9vZ2HD16FGFhYQgJCYGHhwc8PDzQ3NyM4uJihIT81LaLiCZsIs7WOyswzIM53SJitH7atoS5F4jx9NkdDzKZDFu2bMFbb72F3NxcXL16FdOnTxfs88ytQO/q6go3Nze4D5rFlMlkBpMqQ1e2zYmEMHe22d7eHvb29nBzczMIYTeFSqUyWrlYj1arRUlJCe9kV1VV8RVyB+Ph4WEQKu7t7T2u37Z+tUq/OhMVFQUvLy9wHIcTJ06gpKQE69evn/Dq3GLlpptuQllZGerq6nDmzBksXbp03JqOjo7gOA6bNm0yO02CIQ6SkpKQXnUUWiPVyxnWhYigUqlM7iOTyTB37lx+BVuPnZ0dQkJC+Gvv2bNnoVKpcPLkSZw5cwYRC27gw2wH13MgDEyajXe1kSF+hIgMMacyem9vLxoaGqy2cktE+M9//oOenh44OzsbFBL19PSEh4eHwd908uRJdHZ2YsWKFXzhYKEQQ2cFxugwp1tkjNZP21aw5eJvevz8/LB06VKcPHkShw8fRmhoqGD2mDu5EBAQYNKBHYnRIiEsKXZmboXPoTfG7u5utLW18Y/Ozk7k5OQY7GNnZ4eAgAB+oBcYGChIiD/HcXBWKtHY0ABnpRIcx6G7qwvnzp2DTqeb0Ir/YkepVGLdunU4ePAgTp06hejoaEAyvur7YWFh8PHxQXh4uJWsZNgKFRUVozrcmv5+q6SnXO+o1epRHZT+/n50dnaOeKwlEgnmzJmDpKQk5OTk4MyZM6irqzNaS2Mw1lhtZIgfa0eGmDtmysvLQ0lJCR+55+bmNqauMIO76zQ2NiI/P5+vVj70Nz34uUajwZkzZ6DVapGdnY0VK1YgKemnGhb6grfWmoQQW2cFhnGY080QBDEUfwMG8g8LCgpQU1ODr776Ctu3bxdk8DDUmdc7fEN72I5nltJUJIQ5+a9DMWcFPSkpCb6+vqisrOTzu42tkLu5uRmEifv6+k5YMRJOIoGjQgHu2qp5SUkJdDrdQJj5z6hX+0QQHx+Pq1evoqCgAIcPH8bcDbeO6f1NTU2oqalBfHw8H8VgzmTL0O+QIRzWOtYTGQ55vf8+zD2G5uwnkUgQHx+PuLg4/Pjjj8is/cnpJiJoNBqD1LHe3l42cSIyxHC+mLsAwnEc+vr6+Ir8wIDzOdgJHykM3Fg7VGCgtsho9yU7Ozvs2LEDR44cQX19PQ4fPoy0tDQkrNwATb+JCuMWFn8TW2cFhnGY080QBCGLv1kTqVSKzZs3480330RRURHS09Mxa9Ysq3/O0EmIvr6+YflK1piltHYkxEgr6Pb29nByckJ6errR1WIHBwd4eXnBzc0NLi4uE97veagt+jZqrS2taGpuhkQiwerVq9kNaoxwHIcNGzbgP//5D1avXo26MUQOV1ZWoqSkBABQXV09pt/E4O+QISzWOtYTGQ55vf8+zD2GYznWHMcNfIeDnG6tVguVWj1sX5ZHKi7EcL6Yu7K7aNYstLe3Q6VSQa1Wo62tDb29vaivr+cXG7744guEhobyDzc3txHboQIDaROxsbGjOsghISG4//77cenSJZw4cQL19fXIyMwwuq81ir+JqbMCwzjM6WYIQk5TO+AdiOW37RzxAmErfXa9vb2xYsUKfPfdd/j222+HVfm2BtbMu54o+vv7UVtbC5VKhYCAAGg0GnR3dwMYGGTpB1oODg5wcXGBs7MzX/G8t7cXTU1NcHV1tZkcI9LpUFQ80OrmxhtvhE6nMxkBwCp8GsfZ2Rm33347AKDOjO4HRISCggK+MF9AQADLo78OYOGQE4erqyscHBysfqxZHiljsjB3ZVcqlRoUhdXpdFCr1VCr1VCpVHwEXmZmJjIzMwEMnC+jpZYVFRUZ7Y5izM65c+ciPj4eKSkpqBpl/mm86Rhi6qzAGA5zuhmCIpYLxPz585GXl4fKykp8+eWX2Llzp9VtHLxq3NfXh7CwMJSWlsLe3t7ivOuhDM5PGmuhvfb2doOK4rW1tQaVv4GBG4yfn59B267BRdGCgoLQ1NSE4uJi9PT0ICcnB25ubkhMTLQoV90a9HR3o6SkBE5KJbq6uvjCQQzr0N7eDuW1fPmhaDQaXMm6ApVKBY7jMHXqVIv6oeu/w/DwcMjHkK/HGDvWOtYTGQ55vf8+9OeWtY/10IkTY+0X2cSJ+BDL+WLJyq5EIoG7uztfjFar1WLWrFkoKytDWVkZqquroTYSrTGUsaZNKBQKTJ8+HVVpV62qy/h5wZxuhuCIofWKRCLB5s2b8cYbb6CsrAznz5+Hn5+f1avE6/Ouc3NzkZ2djQ0bNiAmJsbivOvBGMtPGqmQGhGho6PDoOBZenr6ME2FQmFQUTwgIGBY9duhlci9vLzg4eGByspKVFRUQKVS4Y033kBycjJiY2PH9TeOFSJC+7XetPqCQGFhYQZVeBmWU1paioqKCoSHh8PZ2dlgYq27uxtXs7Kg6FRBKpUiNjbWKhNLDPHAwiEnjlGPtQUhrSyPlDHZjHfhRiqVIiIiAhEREQAGnN7Tp0/jzJkzo753rGkT5taxuHr1KpydnaFQKODt7T2m8bG+fZ8188UZEwcbeTIY1/Dw8MCqVatw+PBhfPvttwbbrNkPXSKRICAgANnZ2QgICLCKMz9SflJbWxs+/fRTbN68GXZ2dgZO9tAibhzHwcfHx8DJdnd3t2hAJZFIEBISAl9fX5SUlKC8vHzEHLLxrM6bYmhv2a6uLtjJZMzhtiL6vun6XG09Dg4OCAoKQn9/P+RyOeLj4wWpTs+wfcQS7fRzwOSxtnByY7AzPzjyiU2cMCYKay7c6H+35jjdY02bMDcdQ6vVQqVSQaVSwdHRkf/burq6UFhYCCcnJ1RWVsLb2xs+Pj58NfampiajE2DWyBdnTAxs9MlgDGIkx0DvvG7bts0qjrc10el0OHr0qMl98vPzh70mk8ng4uICV1dXODs7Y9OmTXBwcLCqbXK5HLGxsZgzZw5/4yAiHD16FNOnT0d7e/uIq/PjOc6Db06DnWxNfz9ycnLgFR3GZoWtwEgTGL29vSgqKkJYWBjifVyHRUcwri/EEO30c0GIY6135puamtDY0ICoqCh4eXmxiROGKDGnu44laRPm1rGYFR2Nrq6uYS38Ojo6eGc8NzeXf12pVMLb2xt9bqbrzLD2fbYPc7oZjGvodLphK9xDOXLkCAICAkBE0Gq10Ol00Gq1/GPw85H+r9Vq+ZDsixcvQiqVQqvVwsnJCUTEP3Q6ndH/V1ZWGuj29PSg41oItSnkcjnc3d3h4uICFxcXKBSGM6LWdrgHo1Qq+f/n5ubiwoULuHDhgtF9xzvBQUQoKioyuY+5RVIYI6PT6VBcXGxyn5qaGiQGeE6QRQwGQyg4joOzUonGhgY4j1DDgcEQA0IVtjU3HcPZWWF0VdzV1RXR0dHo7OyERqNBY2Mj1Go1Ojo60NHRAZ940+lZLF/c9mFOtwjIMaNCcKyXeWEtjJGpqKiAPDgS8lH2++c//znuz+I4DjKZDP39/XzPU3MdzKqqKoPnPvFJGDWgSNuFsLCwSStmNpjg4GDMmDEDV65cMblfQUEBJBIJ+vv7jU5gyGQyzJ49m9//yy+/RGNjI6T+oQYzzdr+frS0tBiE07Ob0/ipqKgwOaMPWO8460MC2Yq58IjxWIvRZjHCjvPPg+v9ezSnu44lNRCA8dWxcHBw4LumLFiwAMDAPbSxsRHp6emjVkYHWPs+W4c53QzGNcwtggEMhNVKpVJIJBJIpVL+Mfj5aP8f+pqLiwskEgk4jgPHcSP+f8aMGQbvL1T3jLqyC9hOWxelUomZM2eO6nT39vYiLy9vRMfOcUjV1bq6OtTV1cHH3c/gdQKg1Q1vKM1uTuPD3PPFGseZk0hgL2AkBuMnxHisxWizGGHH+ecB+x4HEKIGwqi6Y8TBwQGBgYHo7+8ftTI6YDvjPIZxfjZO96uvvoo9e/agrq4OCQkJePnllzF37tzJNoshIswtgrFr1y6EhoaO67M6Ojpw9epVxMfH86HX5lYvH9o/urOxDZWVlaLqh2uuw+bg4AAHBwejkxdDby4rV66ERqNBebfOoLCXVCKBwskJXZ2dBs43uzmNj4ns4dvX14eGhgb4+Piw701gxHisxWizGGHH+ecB+x5/Qqh6E9bWNTdf3JbGeRPFhQsXcPbsWXR0dMDPzw/r1q3DlClTJtsso4y/RLANsH//fjz++ON47rnncPnyZSQkJGDNmjVoaGiYbNMYIiI4OHjUvGYHBwcEBweP+7M0Gg0qKiqg0WjGraXv0WoKW2vrYq7DFhYWhpkzZ2LGjBmIi4tDTEwMoqKiEBERgaCgIIN9IyIiEB0djcDAQIPvkZNIIHdwADeoIvr1enOyJuaeL9Y4zjqtFm1qNXRD+sYzrI8Yj7UYbRYj7Dj/PGDfo/jQ54ubwtbGeRPB1atXcezYMSxbtgwPPPAAfH198eGHH6Kzs3OyTTPKz8Lp/n//7//hF7/4Be6++27ExsbijTfegEKhwDvvvDPZpjFEhLkXNWu0s7I2+jyioU6Qg4MD4uLibK5St756qCksddjEOAkhRtgggMFgMBiMiUFs47yJ4Ny5c5g1axZmzpwJb29vbNiwAXZ2dkhPT59s04wi+vDyvr4+pKWl4Xe/+x3/mkQiwcqVK/Hjjz9OomUMMWJWEQwbRaj8JCEQqnqonlF7y7JellZhPEVjGAwGg8FgmI8188XFjlarRU1NDRYvXsy/xnEcwsPDhxUcthVE73Q3NTVBq9UOy3P19fVFXl6e0ff09vYaDBDVajUAoLa2VjhDx0Fzx+ghyFU9apvSFqPNem0OwNSICLS3t6GvTwN7ezs4O7uAA9Dc1GSx9mBUKhWam5tRXV3Nh8Hof4ejMTQk3djxkEokA1W7m5sBAGSG42NpqLs5dhvTdnZ2xtKlS3Hy5EmDHG9nZ2fccMMNAAbO77Hq6o+H/ntsaWlBc1MTvL284OHhAc4M3dG0TWHJsRZKdyK0RztfrHE8ert7oFap0NLUDAdH+bhtHg1b055Im8VwrMVos5DaE2WzseNsLe2RsOXjIRZtdr78PH8fQ8d549G2BfQ+mFqtNoiE1Nf2GUpXVxeICE5OTgavOzk5mTXGmwxE73Rbwl//+lf86U9/GvY6K7zGYNgGf/7znyfbBAaDwWAwGAzGBBIfH2/w/LnnnsMf//jHyTHGyoje6fby8oJUKh1W+bm+vh5+fn5G3/O73/0Ojz/+OP+8v78fubm5CAoKssl83aG0t7cjNjYWOTk5ZhekmkxdsWqL0WYhtcVos5DaYrRZSG0x2iykthhtFlKb2Sx+bTHaLKS2GG0WUluMNgupLUabJxOdToeKigrExsZCJvvJPR2pYKtCoQDHccOKpnV2dvJdgWwN0Tvd9vb2SEpKQkpKCjZv3gxg4ItLSUnBww8/bPQ9xkIVFi1aJLSpVqOtrQ0AMGXKlFGLUdmCrli1xWizkNpitFlIbTHaLKS2GG0WUluMNgupzWwWv7YYbRZSW4w2C6ktRpuF1BajzZPNWLoDSaVSBAQEoKSkBNHR0QAAIkJJSYnNRi6L3ukGgMcffxy7du3C7NmzMXfuXPzzn/9EZ2cn7r777sk2jcFgMBgMBoPBYDAYVmT+/Pk4dOgQAgICMGXKFJw7dw4ajQaJiYmTbZpRfhZO92233YbGxkY8++yzqKurQ2JiIo4ePTqsuBqDwWAwGAwGg8FgMMRNfHw8urq6cPLkSXR0dMDPzw933nknCy8XmocffnjEcPKfGw4ODnjuuedGzHOwNV2xaovRZiG1xWizkNpitFlIbTHaLKS2GG0WUpvZLH5tMdospLYYbRZSW4w2C6ktRpvFyNy5c202nHwoHBHRZBvBYDAYDAaDwWAwGAzGzxHbL9XNYDAYDAaDwWAwGAyGSGFON4PBYDAYDAaDwWAwGALBnG4Gg8FgMBgMBoPBYDAEgjndDAaDwWAwGAwGg8FgCARzuhkMBoPBYDAYDAaDwRAI5nQzGAzGGOnt7UVvb+9km3FdcfLkSXR3d0+2GWbT29uL4uJiUf5O6uvrUVdXZxUtrVaL+vp6NDY2WkVvMGq1Gvn5+cjPz4darba6vpghImi1Wqvrvvfee6I71oWFhUhJSUFRUdFkm2KSod/XhQsXcO7cOatdQyoqKnD+/HlcvHgRzc3NVtHUw+6JE4/Y7okM5nSLhrq6Onz55Zd488038eabb+LLL7+02qBoJDo7O/HDDz8I+hmWMvTmdP78efzwww/QaDRW/6y7774bNTU1VtXUaDQoLCy0+uBFpVLhrbfewh/+8Ae8/fbb49JPS0uzomWGNDQ04Pvvv+ftq6+vx9///ne8+OKLyMrKGrd+SUkJ9u7di7/97W/Ys2cPPv/8c7S1tY1L87vvvsNNN90Ed3d3KBQKKBQKuLu746abbsLx48fHbfNI5ObmIjw83OL3Z2Zm4i9/+Qtee+01NDU1GWxra2vDPffcY5Hu22+/jV27duHdd98FAOzfvx8xMTEIDw/Hc889Z7G9I7F69WqUlZWNS6OhocHgeUZGBnbt2oVFixYhOTkZJ0+etEj3vffew48//ggA6Onpwb333gsnJydERUVBqVTiwQcftHhAOn36dPz3f/83KisrLXq/KVpaWpCcnIzg4GA89NBD0Gq1uO++++Dv748pU6Zg4cKFqK2ttUj7P//5D5YuXQonJycEBATAz88Pbm5u2LFjByoqKsZl99tvv43Y2Fh4eHggNjbW4P///ve/x6U9EpmZmZBKpRa///Dhw7jvvvvw5JNPIi8vz2Bba2srVqxYYZFuf38/nnnmGSxbtow/7/bs2QOlUgmFQoFdu3ahr6/PYruHcv/994/7fnjhwgWDe/g333yDZcuWYcqUKZg9ezb27t1rsfZf//pXpKSkABg4ritXrsS0adOwatUqTJs2DevWrYNKpRqzrrOzM+69916cPXvWYttGory8HLNnz4aDgwPWrVuHtrY2rFq1CvPnz8fChQsRGxuLgoICi/Vfe+01hISEICwsDAsXLsT8+fPh4+ODxYsXj+s+z+6JhojxnsiYYIhh03R0dNCdd95JUqmUZDIZ+fj4kI+PD8lkMpJKpXTXXXdRZ2enIJ+dkZFBEolkzO/r6+uj3/zmNxQREUFz5syhf//73wbb6+rqLNIlIqqpqaFFixaRVCqlpUuXUktLC61fv544jiOO4ygqKopqamos0s7MzDT6sLOzoy+++IJ/Plb+9re/UVdXFxER9ff30xNPPEH29vYkkUhIJpPR3XffTX19fRbZvGXLFvrss8+IiOjq1avk5eVF3t7eNG/ePPL19SU/Pz/KycmxSJvjOIqIiKDnn3+eqqurLdIwxokTJ8jJyYk4jiM/Pz/KyMigwMBAioyMpGnTppGDgwN9++23Fml3dHRQcnIy/3uQSCTk5+dHUqmUlEolvfLKKxbpvvfeeySTyej222+nd999lw4fPkyHDx+md999l+644w6ys7OjvXv3WqQ9Gpaeh0RE3377Ldnb21NcXBwFBweTp6cnff/99/x2S8/F//3f/yUnJye65ZZbyN/fn/7yl7+Qp6cn/eUvf6E//elP5OLiQm+++aZFNs+cOdPog+M4iomJ4Z9bgkQiofr6eiIiOnPmDNnZ2dGyZcvoN7/5Da1atYpkMhmdOnVqzLphYWF07tw5IiL6r//6LwoNDaWDBw9Sbm4uHTp0iKKioug3v/mNRTZzHEeenp4klUppzZo1dODAAdJoNBZpDeWee+6h+Ph4evnll2nZsmW0adMmmjFjBqWmptLZs2dpzpw5tHPnzjHr7t27l5ydnemJJ56g3//+9+Tn50dPPfUUvf7667Rs2TLy8vKigoICi2z++9//TgqFgp566ik6ceIE5eTkUE5ODp04cYJ+97vfkZOTE+3Zs8cibVNkZGQQx3EWvXffvn0klUpp/fr1tHjxYpLL5fThhx/y28dzT3zmmWfI19eXHn/8cYqNjaUHH3yQgoKC6MMPP6T333+fpkyZQn/729/GrOvu7m70wXEcubq68s8tYfB5+NVXX5FEIqGdO3fSq6++Svfddx/JZDI6ePCgRdqBgYF0+fJlIiK67777aObMmXT58mXq7u6mjIwMmj9/Pt17771j1uU4juLi4ojjOIqOjqZ//OMf1NDQYJGNQ7n11ltp2bJl9PXXX9O2bdto0aJFdMMNN1BVVRXV1NTQmjVraPPmzRZp79mzhwICAujll1+mt956i2JiYujPf/4zHTlyhHbs2EEKhYIuXrw4Zl12TzRErPdExsTCnG4b595776XIyEg6evQo9ff386/39/fTt99+S1FRUXTfffcJ8tmWXtiee+458vX1pT179tDvf/97cnV1pfvvv5/fXldXZ/HgZceOHbRw4UL66quv6LbbbqOFCxfSkiVLqKqqisrLy2nRokX0q1/9yiJtvZOmd9gGP/SvW3I8Bg8w9uzZQ+7u7vTOO+9QdnY2ffjhh+Tj42PRoIhoYGCUm5tLRETr1q2j7du3U29vLxENTH7ce++9tHr1aou0OY6jX/ziF/wkz/r16+mLL74w+B1awuLFi+lXv/oVtbe30549e2jKlCkG39l//dd/0cKFCy3Svv/++2nRokWUlZVFhYWFlJycTE8++SR1dnbSv//9b1IoFLRv374x60ZGRpp02F999VWaOnWqRTY/9thjJh933XWXxQOMBQsW0NNPP01ERDqdjv72t7+RUqmkI0eOEJHlA4zo6Gj+OF6+fJlkMhm9/fbb/Pa3336bkpKSLLJZJpPR2rVr6Y9//CP/eO6550gikdAvf/lL/jVL4DiOPxdXrVpF99xzj8H2Rx55hFasWDFmXQcHByovLycioqioKP746jl16hQFBwdbbHN1dTV98cUXdPPNN5NMJiNvb2964oknLJ5Q0+Pv709nzpwhop+uy8eOHeO3p6am0pQpU8asGx0dTZ988gn//OLFixQYGEg6nY6IiG677TbasmWLRTYHBwfT/v37R9z+ySefUFBQ0Jh1t2zZYvKxYsUKi8/DxMREeumll/jn+/fvJycnJ/6cGY/THR4eTl9//TURERUWFpJEIjE49vv376f4+Pgx6yqVSlq/fj299957/OPdd98lqVRKzz//PP+aJQw+DxcvXkxPPfWUwfbnn3+e5s+fb5G2g4MDlZWVERFRaGjosEm0S5cukb+/v8U2Z2Rk0MMPP0weHh5kb29Pt9xyCx0+fJj/bVuCt7c3paenExGRSqUijuPo9OnT/Pa0tDTy9fW1SDs0NJQOHz7MP8/PzydPT09+4u7/+//+P1q1atWYddk90RCx3hMZEwtzum0cNzc3flBkjNTUVHJzc7NIe6SZbP3DxcXFoovP1KlT+UEA0cBAYOrUqbR7927S6XTjGmD4+/vTjz/+SEREzc3NxHEcHT9+nN+ekpJC4eHhFmknJCTQ+vXrKTc3l8rKyqisrIxKS0tJJpPRd999x782VgYPMGbOnDlstvPDDz+kuLg4i2x2dHSkoqIiIho4NvoZfj35+fnk6upqkbbebo1GQwcOHKCbbrqJpFIp+fr60pNPPkn5+fkW6bq4uPA2azQakslk/ICDiKigoMBim728vOjSpUv885aWFpLL5Xw0yCuvvEKJiYlj1nVwcKC8vLwRt+fl5ZFcLh+7wTQwKTNr1iy64YYbjD5mz55t8fky+Fjr2bdvHzk5OdHXX39t8bno6OjIO5lEA8fn6tWr/PPCwkKLr0upqakUERFBzz77LGm1Wv51mUxG2dnZFmnqGXwuDr6W6NFHi4yVkJAQfrVkypQpw1aOcnJyyMnJadw2Ew1E+7zwwgsUGRlJEomEFixYMCyayFwUCoXBNc3Ozo6ysrL45yUlJRbZ7ejoSKWlpQavyWQyPmLm/PnzFv8+5HK5ycmG7OxscnR0HLOuTCajdevW0e7du40+Nm7caPF56OTkRCUlJQavff/996RUKun1118f1z1RLpdTRUWFwXP9RCzRwHfo7Ow8Zt3CwkI+0qG9vZ1/3drnoY+Pj8E1m2jgemrp7yMqKoq++eYbIhqIQBk6fkpPTycXF5dx2UxE1NPTQx999BHdeOONJJFIKDAwkP7whz9YZLOzszP/+9BqtSSTySgjI4PfXlhYaNF3SDRwjg8+F3U6HclkMj4iMCMjg5RK5Zh12T3RELHeExkTC3O6bRwXFxeToT8XLlyw6AZCNHAxfuKJJwxmsgc//vSnP1l88Rk64KqqqqKoqCi68847qbq62moDDCcnJyosLOSfl5eXWzTgIiLq7e2lRx55hGJjYw2c1/Fe2DiO48PQPD09DQa1RAODIoVCYZH2vHnz6P/+7/+IaMCh/+KLLwy2Hzt2jPz8/CzSHjrIIBr4Hv/85z9TeHg4SSQSWrJkyZh1vby8+JtRZ2cnSSQSA+cnMzPTIseHaGCSanDYal9fH8lkMv74FxQUWDQQmDVrlsnw4CeffJJmzZo1doNpYJD4wQcfjLg9PT3d4vPF29t72ICWiOjjjz8mhUJBr7/+ukXanp6eBo5PYGCggfNWWFho0UBOj0qlottvv53mzZvHD5CsNdgvKioitVpNYWFhwyapioqKLDoXn376aVqwYAG1trbSU089RTfffDPvqHR2dtK2bdssjjgZHCkzlBMnTtBdd91lsUOfkJDAr1YdPnyYnJ2d6X/+53/47a+//rpFq6QxMTF82gvRwEqdvb09HyVTWFhosc1LliyhnTt3Gg2x7+/vp507d9LSpUvHrDt9+nSDlamhjOc8NDbBQ0R08uRJUiqV9Pvf/95ibV9fX7py5Qr/fOHChVRVVcU/z83NtXiMoNFo6Mknn6SIiAhKTU0lIuudhydOnKDMzEwKCQmhCxcuGGzPy8uz+PqxZ88eiomJocLCQvqf//kfWrBgAX8NKSkpoRtuuIGSk5PHrGvqPCwtLaVnnnnGoggLIqL58+fTM888Q0RE77zzDvn6+hqs/v/5z3+2eJU0MTGRHyMQDSxMKBQKfmU+Ly/PIoee3RMNEes9kTGxMKfbxtm+fTufkzSUy5cvU1JSEt15550WaS9cuJD++c9/jrjd0vDysLAwg9VnPdXV1RQVFUWrVq2y+IIZHBxM58+f55//9re/pebmZgObLXXY9Bw+fJgCAwPphRde4Gedx+t0P//88/TSSy+Rv7//sHC3zMxMi3PjvvnmG/Lw8KB3332X3n33XQoNDaW3336bzpw5Q++88w4FBQVZnEtqapBBRHT8+HHavn37mHU3bdpEGzZsoNTUVLr//vtp9uzZtH79euro6KDOzk5KTk6mtWvXWmTzqlWrDELV9+zZYxBKePnyZYt+H/o89OnTp9Njjz1GL774Ir344ov02GOP0YwZM0ipVFqUC0w0cI4/+uijI24fTy7pqlWrRsxv/eijj8jOzs6ic3HRokUGIaxD+frrry1y1obyzjvvkJ+fH7355ptkZ2dnlcG+RCLh00UGD0aJiL788kuLQiJ7e3tp48aN5O7uTqtWrSK5XE4KhYIiIyPJycmJgoODLY4MMTb5NRS1Wm2R9ocffkhSqZSmTp1KDg4O9Nlnn1FAQABt27aNbr/9drK3t7eoDsIrr7xCrq6u9OSTT9Kzzz5LAQEBBnm0H374ocU5iJmZmeTn50eenp60ZcsWevDBB+nBBx+kLVu2kKenJ/n7+w+b2DSH3bt30y9/+csRt+fk5FBoaKhFNm/atImeffZZo9v01xZL74nLly83Geb96aefWuyw6UlJSaHg4GD63e9+Z9XzUJ++9b//+78G2z/++GOKjY21WP/Xv/412dnZUXR0NMnlcpJIJHwdldmzZ1Ntba1FNo92HloaYn706FGSy+Vkb29PcrmcTp06RVFRUTR37lyaP38+SaVSkykVpti/fz/Z2dnRtm3baOfOnaRUKg0c+jfeeIMWLFgwZl12TzRErPdExsTCnG4bp6WlhdauXUscx5GHhwdFR0dTdHQ0eXh4kEQioXXr1lFra6tF2s8//7zJPJCKigravXv3mHXvvffeYbmSeqqqqmjq1KkWDzA2btxocqLglVdesSgncyh1dXW0bt06WrJkybid7pCQEAoNDeUfQwcY//znPy3OXyMiOnDgAAUGBg7LR5fL5fToo49anINtziDDEgoKCigyMpIvAlJVVUUbN24kmUzG56umpaVZpJ2WlkYeHh7k5+dHwcHBZG9vTx9//DG//ZVXXrGoMBTRwGrGk08+SUuXLqWoqCiKioqipUuX0m9/+9thkR1joba21qK0BXM4ePCgycHLvn376IYbbhizbmpqqkFKwFBeffVVevnll8esa4yCggKaM2cOcRw37gHGyZMnDR5DHeF//vOf9Pe//91i/SNHjtAvf/lLWrt2La1evZp27dpF//d//0cdHR0Wa+7evZva2tosfv9opKam0j/+8Q8+DDc7O5t27NhBt956q8U5u0REr732Gi1cuJCSkpLo6aefpu7ubn5bQUGBQQj0WGlra6PXXnuNdu7cSatXr6bVq1fTzp076fXXX7d4AqKnp0ewoqQnT56kF154YcTt33//vUX3WqKBFKKhoeuD2bdvn8UO22Camppoy5Yt5ObmZjKs2Bz0qVr6R1NTk8H2999/n95///1xfUZOTg79/e9/pwcffJDuv/9+eu655+jYsWMWO8Z//OMfBft9EA3cXw4cOMDfS+rq6ugPf/gDPfHEEwaFvizh8OHDtH37drr11luHTTQ2NTUNO/5jsZndEwcQ6z2RMbFwRESTXUGdMTq5ubk4d+4c3ybMz88PCxYsQHR09CRbNpzy8nLk5eVhzZo1RrfX1NTgu+++w65du6z+2RcuXIBCoUB8fLxV9P71r3/hxIkTePnllxEYGGgVzaGcO3cODg4OmDlzpsUaWq0WaWlpKC0thU6ng7+/P5KSkuDs7Gyx5qlTp7Bo0SLIZDKLNUzR3NwMT09P/nlKSgq6u7uxYMECg9fHSm1tLb755hv09vZixYoViI2NtYa5jElEp9Ohvb0dLi4u4Dhuss1hMBgMBmPSYPdEccKcbgaDwTCT/v5+ZGdn85Nf/v7+iImJgZ2dndW1/fz8EBsba9PaYrRZSG0x2iyktpA2j4RGo0FtbS2Cg4Otqtvf34+amhqr64pVW4w2A+z3MZHaDAbDEGGWsBhW5/vvv0dqaipqa2shkUgQHh6OjRs3IjIy0ma1xWizMe2IiAjcfPPN7Hhcx78PnU6HZ599Fq+++irUarXBNldXVzz88MP405/+BIlEcl1oi9FmIbXFaLOQ2kLaPBo5OTmYNWsWtFqtVXWzs7MF0RWrthhtBtjvw5rar732Gg4ePAgPDw888MADuPHGG/ltTU1NmDt3LkpKSiyyS4zaYrSZMcFMbnQ7YzTq6+tp7ty5JJFISCaTkUQioaSkJPLz8yOpVGpxkSwhtcVos5DaYrRZSG0x2vyb3/yGvL296Y033qDS0lLq6uqirq4uKi0tpTfffJN8fHzoySefvG60xWizkNpitFlIbSFtHg1LC4BOlq5YtcVos5DaYrR5PNovvfQSKRQK+tWvfkV33XUX2dvbG9QtGE8bPDFqi9FmxsTDnG4b57bbbqPNmzeTWq2mnp4eevjhh/lCUCkpKeTp6WmysNhkaIvRZiG1xWizkNpitNnX15eOHj064vajR4+Sj4+PRTaLUVuMNgupLUabhdQW0uaZM2eafERHR1s0ABVKV6zaYrRZSG0x2iykdmxsLO3bt49/fubMGfL29uZ7lY/HERSjthhtZkw8LKfbxnF1dcXZs2cRFxcHAOjs7IS7uzuamprg4uKCDz/8EH/5y1+Ql5dnM9pitFlIbTHaLKS2GG12cnLCuXPnMH36dKPbr1y5goULF6Kjo2PMNotRW4w2C6ktRpuF1BbSZrlcjttvvx1hYWFGt9fW1uKtt94ac7isULpi1RajzUJqi9FmIbUVCgVycnIQGhrKv3b16lWsXLkSd999Nx599FEEBARYZLMYtcVoM2MSmGyvn2Eab29vg5YAXV1dJJFI+N7UxcXF5ODgYFPaYrRZSG0x2iykthhtvummm2j16tXU2Ng4bFtjYyOtXbuW1q9fb5HNYtQWo81CaovRZiG1hbQ5KSmJXnvttRG3p6enW7TqI5SuWLXFaLOQ2mK0WUjtoKAg+uGHH4a9np2dTb6+vrRz506LbRajthhtZkw81q9iwrAqixcvxrPPPovOzk5oNBo8/fTTCA8Ph4eHBwCgsbER7u7uNqUtRpuF1BajzUJqi9HmN954AzU1NfD398esWbOwbt06rFu3DrNmzYK/vz9qamrw+uuvW2SzGLXFaLOQ2mK0WUhtIW1etGgR8vPzR9zu7OyMpUuX2oyuWLXFaLOQ2mK0WUjtxYsX4+DBg8Nej42NRUpKCo4cOTJmTTFri9FmxsTDwsttnJKSEqxevRrl5eXgOA5OTk747LPPsHLlSgDAe++9h/z8fPz1r3+1GW0x2iykthhtFlJbjDYDAxWZv/32W5w7d86gBdKCBQuwevXqcVViFqO2GG0WUluMNgupLaTNDAZjcrly5QrS0tJw9913G91+9epVfP7553juueeuC20x2syYeJjTLQK6urqQmpqKvr4+zJ8/H15eXjavLUabhdQWo81CaovRZgaDwWAwGAwGwxKY081gMBhmcuHCBfz4448GK3cLFy7EnDlzrkttMdospLYYbRZSe6JtXrBgAebOnWuTumLVFqPNQmqL0WYhtcVos5DaYrSZMXEwp1sEdHd34+OPP0Zqaipqa2shkUgQHh6OzZs348Ybb7RJbTHaLKS2GG0WUltsNjc0NODWW2/FmTNnEBwcDF9fXwBAfX09KioqsGjRInz++efw8fG5LrTFaLOQ2mK0WUhtZrP4tcVos5DaYrRZSO2GhgbccsstOHv2rCA2i01bjDYzJoHJq+HGMIfCwkIKCQkhHx8fCgoKIo7jaP369TRv3jySSqW0detW0mg0NqUtRpuF1BajzUJqi9HmW2+9lRYsWEB5eXnDtuXl5dHChQspOTnZIpvFqC1Gm4XUFqPNQmozm8WvLUabhdQWo81CaovRZiG1xWgzY+JhTreNs27dOnrggQdIp9MREdGLL75I69atIyKigoICCg0Npeeee86mtMVos5DaYrRZSG0x2qxUKuny5csjbr906RIplUqLbBajthhtFlJbjDYLqc1sFr+2GG0WUluMNgupLUabhdQWo82MiYc53TaOQqGggoIC/nlvby/Z2dlRU1MTEREdOnSIQkNDbUpbjDYLqS1Gm4XUFqPNnp6edPLkyRG3nzhxgjw9PcdusEi1xWizkNpitFlIbWaz+LXFaLOQ2mK0WUhtMdospLYYbWZMPMzptnECAgIoLS2Nf97a2kocx1FbWxsREZWUlJCDg4NNaYvRZiG1xWizkNpitPmXv/wlhYSE0MGDB0mtVvOvq9VqOnjwIIWGhtLDDz9skc1i1BajzUJqi9FmIbWZzeLXFqPNQmqL0WYhtcVos5DaYrSZMfEwp9vG2bVrFy1btoxyc3OppKSEbrvtNpo5cya//eTJkxQUFGRT2mK0WUhtMdospLYYbe7p6aEHH3yQ7O3tSSKRkFwuJ7lcThKJhOzt7emhhx6inp4ei2wWo7YYbRZSW4w2C6nNbBa/thhtFlJbjDYLqS1Gm4XUFqPNjImHVS+3cRoaGrBp0yacP38eHMchKCgIX3zxBWbOnAkAOHDgAGpra/HrX//aZrTFaLOQ2mK0WUhtMdqsp62tDWlpaQYtO5KSkuDi4mKRnti1xWizkNpitFlIbWaz+LXFaLOQ2mK0WUhtMdospLYYbWZMHMzpFgmFhYXo7e1FdHQ0ZDKZKLTFaLOQ2mK0WUhtMdrMYDAYDAaDwWCMFclkG8Awj8jISMTHxw9zICorK3HPPffYpLYYbRZSW4w2C6ktNpu7u7uRmpqKnJycYdt6enqwd+9ei3TFqi1Gm4XUFqPNQmozm8WvLUabhdQWo81CaovRZiG1xWgzY4KZ3Oh2xnjJyMggiUQiKm0x2iykthhtFlLbFm3Oz8+nkJAQ4jiOJBIJLV26lKqrq/ntdXV1FtssRm0x2iykthhtFlKb2Sx+bTHaLKS2GG0WUluMNgupLUabGRMPi7u0cb766iuT20tKSmxOW4w2C6ktRpuF1Bajzb/97W8RHx+PS5cuQaVS4dFHH8XixYtx8uRJBAcHW6QpZm0x2iykthhtFlKb2Sx+bTHaLKS2GG0WUluMNgupLUabGZPAZHv9DNPoZ7Y4jhvxYekMl1DaYrRZSG0x2iykthht9vHxoStXrvDPdTodPfjggxQcHEzFxcXjmmkWo7YYbRZSW4w2C6nNbBa/thhtFlJbjDYLqS1Gm4XUFqPNjImHOd02TkBAAB06dGjE7enp6RafbEJpi9FmIbXFaLOQ2mK02dnZmXJycoa9/qtf/YoCAwPphx9+sNhmMWqL0WYhtcVos5DazGbxa4vRZiG1xWizkNpitFlIbTHazJh4WHi5jZOUlIS0tDRs2rTJ6HaO40AWFqAXSluMNgupLUabhdQWo83R0dG4dOkSYmJiDF5/5ZVXAAAbN24cu7Ei1hajzUJqi9FmIbWZzeLXFqPNQmqL0WYhtcVos5DaYrSZMQlMlrfPMI8ffviBjhw5MuL2jo4OOnnypE1pi9FmIbXFaLOQ2mK0+YUXXqB169aNuP2hhx4ijuPGrCtWbTHaLKS2GG0WUpvZLH5tMdospLYYbRZSW4w2C6ktRpsZEw/r081gMBgMBoPBYDAYDIZAsD7dDAaDwWAwGAwGg8FgCARzuhkMBoPBYDAYDAaDwRAI5nQzGAwGg8FgMBgMBoMhEMzpZjAYDAaDwWAwGAwGQyCY081gMBgMBoPBYDAYDIZAMKebwWAwGAwGg8FgMBgMgWBON4PBYDAYDAaDwWAwGALBnG4Gg8FgMBgMBoPBYDAE4v8H2dgdoC/17kMAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "thlg-bcXDxMA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 12) Generating dataset for analysis"
      ],
      "metadata": {
        "id": "lqXHXFdCEM98"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Load the datasets\n",
        "df_1 = pd.read_feather('/content/gdrive/MyDrive/TimePressure/migration_sample_with_sentence_predictions_h.feather')\n",
        "df_2 = pd.read_feather('/content/gdrive/MyDrive/TimePressure/migration_sample_with_sentence_predictions_e.feather')\n",
        "df_3 = pd.read_feather('/content/gdrive/MyDrive/TimePressure/migration_sample_with_sentence_predictions_f.feather')\n",
        "df_4 = pd.read_feather('/content/gdrive/MyDrive/TimePressure/migration_sample_with_sentence_predictions_g.feather')\n",
        "\n",
        "# Rename the column in df_2\n",
        "df_2.rename(columns={'resp_f_sentence_predh': 'resp_f_sentence_pred'}, inplace=True)\n",
        "\n",
        "# Select required columns from each dataset\n",
        "df_1_filtered = df_1[['speech_id', 'sentence_id', 'title', 'speaker', 'portfolio', 'sentence', 'year', 'date', 'tp_h_sentence_pred_first_non_negative']]\n",
        "df_2_filtered = df_2[['speech_id', 'sentence_id', 'resp_f_sentence_pred_first_non_negative']]\n",
        "df_3_filtered = df_3[['speech_id', 'sentence_id', 'tp_l_sentence_pred_first_non_negative']]\n",
        "df_4_filtered = df_4[['speech_id', 'sentence_id', 'resp_s_sentence_pred_first_non_negative']]\n",
        "\n",
        "# Merge the datasets on 'speech_id' and 'sentence_id'\n",
        "df_merged = df_1_filtered.merge(df_2_filtered, on=['speech_id', 'sentence_id'], how='left') \\\n",
        "                          .merge(df_3_filtered, on=['speech_id', 'sentence_id'], how='left') \\\n",
        "                          .merge(df_4_filtered, on=['speech_id', 'sentence_id'], how='left')\n",
        "\n",
        "# Display the first few rows of the merged DataFrame\n",
        "print(df_merged.head())\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WS6XpJDfNBP3",
        "outputId": "9196ccdc-d3c9-48da-c218-1070b95ac99d",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   speech_id  sentence_id                                              title  \\\n",
            "0          0            1  ''Avec une vue sur l'extérieur'' – Discours du...   \n",
            "1          0            2  ''Avec une vue sur l'extérieur'' – Discours du...   \n",
            "2          0            3  ''Avec une vue sur l'extérieur'' – Discours du...   \n",
            "3          0            4  ''Avec une vue sur l'extérieur'' – Discours du...   \n",
            "4          0            5  ''Avec une vue sur l'extérieur'' – Discours du...   \n",
            "\n",
            "               speaker  portfolio  \\\n",
            "0  jean claude juncker  President   \n",
            "1  jean claude juncker  President   \n",
            "2  jean claude juncker  President   \n",
            "3  jean claude juncker  President   \n",
            "4  jean claude juncker  President   \n",
            "\n",
            "                                            sentence  year       date  \\\n",
            "0  Caro Antonio, Cari amici, Mr. Minister, Mr. Ma...  2017 2017-05-05   \n",
            "1  I am hesitating between English and French, bu...  2017 2017-05-05   \n",
            "2  And also because the French will have election...  2017 2017-05-05   \n",
            "3  But before I do this, I have to tell you that ...  2017 2017-05-05   \n",
            "4  And so I can't make a masterful speech, but I'...  2017 2017-05-05   \n",
            "\n",
            "   tp_h_sentence_pred_first_non_negative  \\\n",
            "0                                      0   \n",
            "1                                      0   \n",
            "2                                      0   \n",
            "3                                      0   \n",
            "4                                      0   \n",
            "\n",
            "   resp_f_sentence_pred_first_non_negative  \\\n",
            "0                                        0   \n",
            "1                                        0   \n",
            "2                                        0   \n",
            "3                                        0   \n",
            "4                                        0   \n",
            "\n",
            "   tp_l_sentence_pred_first_non_negative  \\\n",
            "0                                    0.0   \n",
            "1                                    0.0   \n",
            "2                                    0.0   \n",
            "3                                    0.0   \n",
            "4                                    0.0   \n",
            "\n",
            "   resp_s_sentence_pred_first_non_negative  \n",
            "0                                        0  \n",
            "1                                        0  \n",
            "2                                        0  \n",
            "3                                        0  \n",
            "4                                        0  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the aggregated data to an Excel file\n",
        "df_merged.to_excel('/content/gdrive/MyDrive/TimePressure/commission_invocation.xlsx', index=False)"
      ],
      "metadata": {
        "id": "bROc6qqwC0vL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Generate monthly data set for analysis"
      ],
      "metadata": {
        "id": "1dyi6bST2Aad"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Ensure date is in datetime format\n",
        "df_merged['date'] = pd.to_datetime(df_merged['date'])\n",
        "\n",
        "# Create a new month variable in yyyymm format\n",
        "df_merged['month'] = df_merged['date'].dt.strftime('%Y%m')\n",
        "\n",
        "# Generate a complete date range for all months within the range of your dataset\n",
        "all_months = pd.date_range(start=df_merged['date'].min(), end=df_merged['date'].max(), freq='MS').strftime('%Y%m')\n",
        "all_months_df = pd.DataFrame(all_months, columns=['month'])\n",
        "\n",
        "# Group by the month variable and aggregate counts for each prediction column\n",
        "monthly_aggregates = df_merged.groupby('month').agg({\n",
        "    'tp_h_sentence_pred_first_non_negative': 'sum',\n",
        "    'resp_f_sentence_pred_first_non_negative': 'sum',\n",
        "    'tp_l_sentence_pred_first_non_negative': 'sum',\n",
        "    'resp_s_sentence_pred_first_non_negative': 'sum'\n",
        "}).reset_index()\n",
        "\n",
        "# Merge with the complete range of months and fill any missing values with zero\n",
        "monthly_aggregates = all_months_df.merge(monthly_aggregates, on='month', how='left').fillna(0)\n",
        "\n",
        "# Convert columns to integer type for count values\n",
        "monthly_aggregates[['tp_h_sentence_pred_first_non_negative',\n",
        "                    'resp_f_sentence_pred_first_non_negative',\n",
        "                    'tp_l_sentence_pred_first_non_negative',\n",
        "                    'resp_s_sentence_pred_first_non_negative']] = monthly_aggregates[\n",
        "    ['tp_h_sentence_pred_first_non_negative',\n",
        "     'resp_f_sentence_pred_first_non_negative',\n",
        "     'tp_l_sentence_pred_first_non_negative',\n",
        "     'resp_s_sentence_pred_first_non_negative']].astype(int)\n",
        "\n",
        "# Display the first few rows of the aggregated monthly data\n",
        "print(monthly_aggregates.head())\n",
        "\n",
        "# Save the aggregated data to an Excel file\n",
        "monthly_aggregates.to_excel('/content/gdrive/MyDrive/TimePressure/tp_invocations_month.xlsx', index=False)\n"
      ],
      "metadata": {
        "id": "Qzpv5roh860z",
        "outputId": "791699fd-07a6-4b3a-e2be-4a9214dc2b64",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    month  tp_h_sentence_pred_first_non_negative  \\\n",
            "0  199002                                      0   \n",
            "1  199003                                      0   \n",
            "2  199004                                      0   \n",
            "3  199005                                      0   \n",
            "4  199006                                      0   \n",
            "\n",
            "   resp_f_sentence_pred_first_non_negative  \\\n",
            "0                                        0   \n",
            "1                                        0   \n",
            "2                                        0   \n",
            "3                                        0   \n",
            "4                                        0   \n",
            "\n",
            "   tp_l_sentence_pred_first_non_negative  \\\n",
            "0                                      0   \n",
            "1                                      0   \n",
            "2                                      0   \n",
            "3                                      0   \n",
            "4                                      0   \n",
            "\n",
            "   resp_s_sentence_pred_first_non_negative  \n",
            "0                                        0  \n",
            "1                                        0  \n",
            "2                                        0  \n",
            "3                                        0  \n",
            "4                                        0  \n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "toc_visible": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
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        "77b9799a06dd472e841c3c461ffc36f7": {
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